Case Study 2 - Employee Attrition
Jon Paugh
11/18/2019
Doing Data Science - Case Study 2
Employee Attribution
Original dataset https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset
Goal - using the subset of data provided for training, predict the salaries (MonthlyIncome) and Attribution (Attrition = Yes/No) for the two separate validation datasets.
Must use either KNN or Naive Bayes, but can use other techniques too
Install packages
require("e1071")## Loading required package: e1071require("caret")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2require("corrplot")## Loading required package: corrplot## corrplot 0.84 loadedrequire("MASS")## Loading required package: MASSrequire("randomForest")## Loading required package: randomForest## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
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## marginrequire("fields")## Loading required package: fields## Loading required package: spam## Loading required package: dotCall64## Loading required package: grid## Spam version 2.4-0 (2019-11-01) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.##
## Attaching package: 'spam'## The following objects are masked from 'package:base':
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## backsolve, forwardsolve## Loading required package: maps## See https://github.com/NCAR/Fields for
## an extensive vignette, other supplements and source coderequire("ggthemes")## Loading required package: ggthemesrequire("ROCR")## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
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## lowessrequire("dplyr")## Loading required package: dplyr##
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## combine## The following object is masked from 'package:MASS':
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## intersect, setdiff, setequal, unionrequire("ggplot2")
require("scales")## Loading required package: scalesrequire("grid")
require("reshape")## Loading required package: reshape##
## Attaching package: 'reshape'## The following object is masked from 'package:dplyr':
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## renameFirst lets get our data and see how many rows we have.
note that data at the kaggle project has a total of 1470 rows, let’s see if we are dealing with the same data set?
We see that we are, which means this dataset was created probably by taking the kaggle dataset, splitting off two 300 row setsets, taking the attrition off one of them, and taking the monthlyIncome off the other
one other difference is that the No Salary and No Attrition data have an “ID” column added. Not sure why this was necessary since the employee rows already contain an employee ID (which should be unique), but that’s fine.
fullTraining = read.csv("CaseStudy2-data.csv")
nrowsFullTraining = nrow(fullTraining)
ValidationNoAttrition = read.csv("CaseStudy2CompSet No Attrition.csv")
nrowsValidationNoAttrition = nrow(ValidationNoAttrition)
ValidationNoSalary = read.csv("CaseStudy2CompSet No Salary.csv")
nrowsValidaitonNoSalary = nrow(ValidationNoSalary)
print(paste("Number of rows : Full= ",nrowsFullTraining," ValidationNoAttrition = ",nrowsValidationNoAttrition, " ValidationNoSalary = ", nrowsValidaitonNoSalary))## [1] "Number of rows : Full= 870 ValidationNoAttrition = 300 ValidationNoSalary = 300"print(paste("Total rows " ,nrowsFullTraining + nrowsValidationNoAttrition + nrowsValidaitonNoSalary ))## [1] "Total rows 1470"let’s try merging to a single dataframe ….
1470 rows
suppressMessages(library(plyr))
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## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## 1 Married 3407 25348 1 Y
## 2 Married 3600 8429 1 Y
## 3 Divorced 2377 19373 1 Y
## 4 Single 16659 23258 2 Y
## 5 Divorced 8620 23757 1 Y
## 6 Single 7484 25796 3 Y
## 7 Divorced 3482 19788 2 Y
## 8 Married 5377 3835 2 Y
## 9 Single 6812 17198 1 Y
## 10 Married 9824 22908 3 Y
## 11 Divorced 3617 25063 8 Y
## 12 Single 2501 18775 1 Y
## 13 Divorced 3055 6194 5 Y
## 14 Married 17639 6881 5 Y
## 15 Single 6388 22049 2 Y
## 16 Single 4332 14811 1 Y
## 17 Single 4320 24152 1 Y
## 18 Divorced 16124 3423 3 Y
## 19 Single 5160 21519 4 Y
## 20 Married 7654 5860 1 Y
## 21 Single 2837 15919 1 Y
## 22 Divorced 17399 6615 9 Y
## 23 Single 4440 25198 6 Y
## 24 Married 2145 2097 0 Y
## 25 Divorced 10851 19863 2 Y
## 26 Single 9980 15318 1 Y
## 27 Married 6430 20794 6 Y
## 28 Single 3447 24444 1 Y
## 29 Married 5507 16822 2 Y
## 30 Single 2422 25725 0 Y
## 31 Single 4930 13970 0 Y
## 32 Single 5562 9697 6 Y
## 33 Single 7880 2560 0 Y
## 34 Married 2705 9696 0 Y
## 35 Single 1416 17258 1 Y
## 36 Married 5301 2939 8 Y
## 37 Married 13525 14864 5 Y
## 38 Single 5399 14511 4 Y
## 39 Married 19272 21141 1 Y
## 40 Single 18265 8733 6 Y
## 41 Single 4317 2302 3 Y
## 42 Single 3117 26009 1 Y
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## 44 Single 10512 20002 6 Y
## 45 Married 3346 11873 4 Y
## 46 Married 19161 13738 3 Y
## 47 Married 16555 10310 2 Y
## 48 Married 4189 8800 1 Y
## 49 Single 3500 25470 0 Y
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## 51 Married 16856 10084 1 Y
## 52 Married 2909 23159 1 Y
## 53 Divorced 2314 9148 0 Y
## 54 Married 4936 14862 4 Y
## 55 Married 5902 14590 4 Y
## 56 Divorced 19197 8213 1 Y
## 57 Single 2686 5207 1 Y
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## 60 Married 2818 5044 2 Y
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## 64 Single 13758 2447 0 Y
## 65 Single 11244 21072 2 Y
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## 70 Married 2372 5628 6 Y
## 71 Single 6540 19394 9 Y
## 72 Single 4541 7744 1 Y
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## 74 Married 4284 13588 5 Y
## 75 Married 5562 21782 4 Y
## 76 Single 2080 4732 2 Y
## 77 Married 6755 2967 2 Y
## 78 Single 4968 26427 0 Y
## 79 Married 3692 9256 1 Y
## 80 Married 2143 25527 4 Y
## 81 Married 5429 17491 4 Y
## 82 Divorced 9725 12278 0 Y
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## 88 Single 19068 21030 1 Y
## 89 Single 10965 12066 8 Y
## 90 Single 5326 3064 6 Y
## 91 Married 7879 14810 1 Y
## 92 Married 6472 8989 1 Y
## 93 Single 3445 6152 1 Y
## 94 Married 18172 9755 3 Y
## 95 Married 4639 11262 1 Y
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## 98 Married 5151 12315 1 Y
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## 100 Single 1261 22262 1 Y
## 101 Single 13591 14674 3 Y
## 102 Single 8474 20925 1 Y
## 103 Married 3691 4605 1 Y
## 104 Married 17584 21016 3 Y
## 105 Married 2033 14470 1 Y
## 106 Married 5472 3334 1 Y
## 107 Married 5562 19711 3 Y
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## 112 Single 8095 18264 0 Y
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## 114 Married 6261 4185 3 Y
## 115 Married 17875 11761 4 Y
## 116 Married 6804 23683 3 Y
## 117 Married 3579 9369 0 Y
## 118 Married 7639 24525 1 Y
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## 120 Single 13872 24409 0 Y
## 121 Married 4440 7636 0 Y
## 122 Married 7969 19609 2 Y
## 123 Married 19331 19519 4 Y
## 124 Married 2571 12290 4 Y
## 125 Married 4639 2261 2 Y
## 126 Married 11836 22789 5 Y
## 127 Single 2642 2755 1 Y
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## 129 Married 19406 8509 4 Y
## 130 Married 4374 15411 0 Y
## 131 Married 2339 3666 8 Y
## 132 Married 16328 22074 3 Y
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## 134 Single 9907 26186 7 Y
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## 138 Married 4841 24052 4 Y
## 139 Married 4621 5869 1 Y
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## 142 Divorced 18430 16225 1 Y
## 143 Single 7945 19948 6 Y
## 144 Single 3038 12430 3 Y
## 145 Divorced 7441 20933 1 Y
## 146 Single 9355 9558 1 Y
## 147 Single 4272 9558 4 Y
## 148 Single 1359 16154 1 Y
## 149 Single 3310 4488 1 Y
## 150 Married 19586 23037 1 Y
## 151 Married 2911 14776 1 Y
## 152 Single 2450 10919 2 Y
## 153 Married 13503 14115 1 Y
## 154 Single 9667 2739 9 Y
## 155 Single 2342 22929 0 Y
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## 161 Married 6516 5041 2 Y
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## 165 Married 5231 23726 2 Y
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## 168 Married 6349 22107 0 Y
## 169 Married 3221 3297 1 Y
## 170 Single 4859 6698 1 Y
## 171 Single 3424 21632 7 Y
## 172 Married 2362 14669 4 Y
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## 177 Married 3580 10554 2 Y
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## 179 Married 19513 9358 4 Y
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## 182 Divorced 2645 21923 1 Y
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## 214 Married 5175 22162 5 Y
## 215 Divorced 2576 20490 3 Y
## 216 Married 2308 4944 0 Y
## 217 Married 2451 22376 6 Y
## 218 Married 6179 21057 1 Y
## 219 Divorced 2904 16092 1 Y
## 220 Single 7779 23238 2 Y
## 221 Divorced 6118 5431 1 Y
## 222 Divorced 2297 17967 1 Y
## 223 Married 19232 4933 1 Y
## 224 Single 19537 6462 7 Y
## 225 Married 4775 19146 6 Y
## 226 Married 7632 14295 4 Y
## 227 Single 4249 2690 1 Y
## 228 Married 5689 24594 1 Y
## 229 Married 7457 13273 2 Y
## 230 Divorced 3034 26914 1 Y
## 231 Divorced 2074 26619 1 Y
## 232 Married 4194 14363 1 Y
## 233 Divorced 19190 17477 1 Y
## 234 Single 4230 19225 0 Y
## 235 Married 5617 21075 1 Y
## 236 Single 2506 13301 3 Y
## 237 Single 2496 6670 4 Y
## 238 Married 9715 7288 3 Y
## 239 Single 19045 18938 0 Y
## 240 Single 16835 9873 3 Y
## 241 Single 3375 17624 0 Y
## 242 Single 2718 17674 2 Y
## 243 Married 19237 12853 2 Y
## 244 Single 9278 20763 3 Y
## 245 Divorced 5605 8504 1 Y
## 246 Married 5488 20161 1 Y
## 247 Married 3722 21081 6 Y
## 248 Married 2659 17759 1 Y
## 249 Single 7446 8931 1 Y
## 250 Married 3506 6020 0 Y
## 251 Single 2313 2993 4 Y
## 252 Divorced 19973 20284 1 Y
## 253 Divorced 19141 8861 3 Y
## 254 Married 1706 16571 1 Y
## 255 Married 17465 15596 3 Y
## 256 Single 10496 2755 6 Y
## 257 Married 6434 5118 4 Y
## 258 Single 4695 12858 7 Y
## 259 Single 5828 8450 1 Y
## 260 Divorced 16823 18991 2 Y
## 261 Divorced 14732 12414 2 Y
## 262 Divorced 9852 8935 1 Y
## 263 Single 17444 20489 1 Y
## 264 Divorced 6728 14255 7 Y
## 265 Divorced 4011 8232 0 Y
## 266 Married 2132 4585 4 Y
## 267 Married 7005 3458 3 Y
## 268 Divorced 5460 6219 4 Y
## 269 Single 3646 17181 2 Y
## 270 Divorced 10388 6975 1 Y
## 271 Married 5405 4244 2 Y
## 272 Married 1601 3445 1 Y
## 273 Single 9936 3787 0 Y
## 274 Single 2716 25422 1 Y
## 275 Single 2564 18437 1 Y
## 276 Single 2062 19384 3 Y
## 277 Single 4294 11148 1 Y
## 278 Married 7510 16873 1 Y
## 279 Married 9610 3840 3 Y
## 280 Single 2871 23785 1 Y
## 281 Divorced 2367 18779 5 Y
## 282 Divorced 2720 18959 1 Y
## 283 Married 10686 8392 6 Y
## 284 Married 2942 12154 2 Y
## 285 Single 5765 17485 5 Y
## 286 Single 3989 20586 1 Y
## 287 Divorced 2778 17725 4 Y
## 288 Single 2478 20938 1 Y
## 289 Divorced 3161 19920 3 Y
## 290 Married 2773 12145 0 Y
## 291 Single 5933 5197 9 Y
## 292 Divorced 2782 19905 0 Y
## 293 Married 2141 5348 1 Y
## 294 Married 5390 13243 2 Y
## 295 Married 2061 11133 1 Y
## 296 Single 2519 12287 4 Y
## 297 Single 5010 24301 1 Y
## 298 Single 2293 10558 1 Y
## 299 Married 19202 15970 0 Y
## 300 Married 4537 17783 0 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 No 17 3 4
## 2 No 13 3 4
## 3 No 20 4 3
## 4 Yes 13 3 3
## 5 No 14 3 3
## 6 No 20 4 3
## 7 No 15 3 2
## 8 No 13 3 4
## 9 No 19 3 2
## 10 No 12 3 1
## 11 Yes 14 3 4
## 12 No 17 3 2
## 13 No 15 3 4
## 14 No 16 3 4
## 15 Yes 17 3 1
## 16 No 12 3 4
## 17 No 13 3 4
## 18 No 14 3 2
## 19 No 16 3 3
## 20 No 18 3 1
## 21 No 13 3 3
## 22 No 22 4 3
## 23 Yes 19 3 4
## 24 No 14 3 4
## 25 Yes 18 3 2
## 26 No 14 3 4
## 27 No 19 3 2
## 28 No 11 3 3
## 29 No 16 3 3
## 30 No 17 3 1
## 31 Yes 14 3 3
## 32 No 14 3 4
## 33 No 18 3 4
## 34 No 16 3 2
## 35 No 13 3 1
## 36 No 15 3 3
## 37 No 14 3 4
## 38 No 12 3 3
## 39 No 15 3 1
## 40 No 12 3 3
## 41 Yes 20 4 2
## 42 No 18 3 3
## 43 Yes 14 3 3
## 44 No 12 3 4
## 45 Yes 20 4 2
## 46 No 15 3 4
## 47 No 13 3 4
## 48 No 14 3 1
## 49 No 14 3 1
## 50 No 13 3 3
## 51 No 11 3 1
## 52 Yes 11 3 3
## 53 No 12 3 2
## 54 No 11 3 3
## 55 No 14 3 3
## 56 Yes 14 3 3
## 57 Yes 13 3 3
## 58 No 15 3 3
## 59 No 11 3 1
## 60 Yes 24 4 3
## 61 No 14 3 4
## 62 No 14 3 4
## 63 Yes 15 3 1
## 64 Yes 12 3 2
## 65 No 25 4 2
## 66 No 15 3 3
## 67 No 18 3 3
## 68 No 13 3 1
## 69 No 13 3 3
## 70 Yes 16 3 4
## 71 No 19 3 3
## 72 No 25 4 2
## 73 Yes 11 3 2
## 74 Yes 22 4 3
## 75 No 13 3 2
## 76 No 11 3 2
## 77 No 11 3 3
## 78 No 16 3 4
## 79 No 12 3 3
## 80 No 13 3 2
## 81 No 13 3 1
## 82 No 11 3 4
## 83 No 16 3 1
## 84 No 11 3 4
## 85 Yes 19 3 4
## 86 Yes 11 3 2
## 87 No 11 3 3
## 88 Yes 18 3 4
## 89 No 24 4 3
## 90 No 17 3 3
## 91 Yes 19 3 2
## 92 Yes 15 3 4
## 93 No 11 3 3
## 94 Yes 19 3 1
## 95 No 15 3 3
## 96 No 18 3 4
## 97 No 20 4 3
## 98 No 25 4 4
## 99 No 14 3 4
## 100 No 12 3 3
## 101 Yes 18 3 3
## 102 No 22 4 3
## 103 Yes 15 3 2
## 104 Yes 16 3 4
## 105 No 18 3 3
## 106 No 12 3 2
## 107 Yes 13 3 4
## 108 Yes 11 3 4
## 109 Yes 22 4 4
## 110 Yes 12 3 3
## 111 Yes 18 3 3
## 112 No 13 3 4
## 113 No 13 3 3
## 114 No 18 3 1
## 115 Yes 13 3 3
## 116 No 18 3 3
## 117 Yes 21 4 1
## 118 No 22 4 4
## 119 No 11 3 3
## 120 No 13 3 3
## 121 No 15 3 1
## 122 Yes 14 3 3
## 123 Yes 16 3 3
## 124 No 17 3 3
## 125 No 16 3 4
## 126 No 14 3 3
## 127 No 11 3 3
## 128 No 15 3 2
## 129 No 11 3 3
## 130 No 15 3 3
## 131 No 11 3 4
## 132 No 13 3 3
## 133 Yes 12 3 4
## 134 Yes 12 3 3
## 135 No 14 3 4
## 136 No 18 3 2
## 137 Yes 17 3 1
## 138 No 14 3 2
## 139 No 19 3 4
## 140 No 15 3 3
## 141 No 18 3 3
## 142 No 13 3 2
## 143 Yes 15 3 4
## 144 No 20 4 1
## 145 No 12 3 1
## 146 No 18 3 3
## 147 No 19 3 1
## 148 No 12 3 2
## 149 No 21 4 4
## 150 No 21 4 3
## 151 No 13 3 3
## 152 No 17 3 4
## 153 No 22 4 4
## 154 No 14 3 2
## 155 Yes 20 4 4
## 156 No 12 3 3
## 157 No 20 4 3
## 158 Yes 12 3 3
## 159 No 14 3 4
## 160 Yes 13 3 3
## 161 Yes 16 3 2
## 162 No 14 3 2
## 163 No 11 3 3
## 164 Yes 13 3 1
## 165 Yes 13 3 2
## 166 No 16 3 4
## 167 Yes 24 4 1
## 168 Yes 13 3 4
## 169 Yes 11 3 3
## 170 No 16 3 4
## 171 No 13 3 3
## 172 No 12 3 3
## 173 No 19 3 1
## 174 No 12 3 3
## 175 No 11 3 2
## 176 No 23 4 4
## 177 No 16 3 2
## 178 No 18 3 4
## 179 Yes 12 3 1
## 180 Yes 18 3 3
## 181 No 14 3 1
## 182 No 12 3 4
## 183 No 15 3 4
## 184 No 11 3 3
## 185 No 11 3 3
## 186 No 20 4 4
## 187 No 11 3 3
## 188 No 12 3 1
## 189 No 17 3 1
## 190 No 11 3 1
## 191 Yes 12 3 2
## 192 No 15 3 4
## 193 No 11 3 4
## 194 No 16 3 1
## 195 No 11 3 3
## 196 No 17 3 3
## 197 Yes 15 3 3
## 198 No 12 3 4
## 199 Yes 11 3 2
## 200 No 11 3 3
## 201 No 17 3 2
## 202 No 11 3 3
## 203 No 14 3 3
## 204 Yes 21 4 3
## 205 Yes 20 4 4
## 206 Yes 23 4 3
## 207 No 16 3 4
## 208 No 15 3 4
## 209 No 12 3 2
## 210 No 11 3 3
## 211 No 22 4 3
## 212 No 14 3 1
## 213 Yes 12 3 4
## 214 No 12 3 3
## 215 No 16 3 2
## 216 Yes 25 4 2
## 217 No 18 3 1
## 218 Yes 15 3 4
## 219 No 12 3 3
## 220 No 20 4 1
## 221 No 13 3 3
## 222 No 14 3 4
## 223 No 11 3 4
## 224 No 13 3 3
## 225 No 22 4 1
## 226 Yes 12 3 3
## 227 Yes 11 3 2
## 228 Yes 14 3 4
## 229 Yes 22 4 3
## 230 No 12 3 3
## 231 Yes 12 3 4
## 232 Yes 18 3 4
## 233 No 14 3 4
## 234 No 15 3 3
## 235 Yes 11 3 3
## 236 No 13 3 3
## 237 No 11 3 4
## 238 No 13 3 3
## 239 Yes 14 3 3
## 240 No 23 4 4
## 241 No 12 3 4
## 242 No 14 3 2
## 243 Yes 11 3 4
## 244 Yes 16 3 4
## 245 No 11 3 1
## 246 Yes 13 3 1
## 247 Yes 13 3 3
## 248 Yes 13 3 3
## 249 No 11 3 1
## 250 Yes 14 3 4
## 251 Yes 20 4 2
## 252 No 22 4 2
## 253 No 15 3 2
## 254 No 11 3 3
## 255 No 12 3 4
## 256 No 15 3 4
## 257 No 17 3 4
## 258 Yes 18 3 3
## 259 Yes 12 3 2
## 260 No 11 3 1
## 261 No 13 3 4
## 262 Yes 19 3 1
## 263 No 11 3 4
## 264 No 12 3 4
## 265 No 18 3 4
## 266 No 20 4 4
## 267 No 15 3 3
## 268 No 22 4 4
## 269 Yes 23 4 2
## 270 Yes 11 3 3
## 271 Yes 20 4 1
## 272 Yes 21 4 3
## 273 No 19 3 2
## 274 No 15 3 4
## 275 No 12 3 3
## 276 No 14 3 2
## 277 No 12 3 2
## 278 No 17 3 2
## 279 No 13 3 3
## 280 No 15 3 3
## 281 No 12 3 1
## 282 No 18 3 4
## 283 No 11 3 2
## 284 No 19 3 2
## 285 No 11 3 1
## 286 Yes 11 3 1
## 287 Yes 13 3 3
## 288 Yes 12 3 2
## 289 Yes 22 4 4
## 290 No 20 4 4
## 291 No 12 3 4
## 292 Yes 13 3 2
## 293 No 12 3 2
## 294 No 14 3 4
## 295 No 21 4 1
## 296 No 21 4 3
## 297 No 16 3 1
## 298 No 12 3 3
## 299 No 11 3 3
## 300 Yes 22 4 1
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 1 80 2 10 3
## 2 80 1 5 2
## 3 80 1 1 0
## 4 80 0 30 2
## 5 80 2 10 3
## 6 80 0 23 1
## 7 80 2 16 3
## 8 80 3 10 3
## 9 80 0 10 2
## 10 80 0 12 2
## 11 80 1 3 2
## 12 80 0 1 4
## 13 80 2 11 2
## 14 80 0 30 3
## 15 80 0 14 6
## 16 80 0 20 2
## 17 80 0 5 2
## 18 80 2 9 2
## 19 80 0 12 3
## 20 80 2 9 3
## 21 80 0 6 3
## 22 80 1 32 1
## 23 80 0 9 3
## 24 80 1 3 2
## 25 80 1 24 2
## 26 80 0 10 3
## 27 80 1 10 4
## 28 80 0 3 2
## 29 80 2 12 1
## 30 80 0 4 3
## 31 80 0 6 2
## 32 80 0 19 3
## 33 80 0 9 3
## 34 80 1 6 2
## 35 80 0 1 6
## 36 80 2 4 2
## 37 80 1 23 2
## 38 80 0 12 3
## 39 80 1 21 2
## 40 80 0 25 3
## 41 80 0 19 2
## 42 80 0 3 2
## 43 80 3 21 2
## 44 80 0 25 6
## 45 80 1 9 3
## 46 80 0 28 3
## 47 80 0 31 2
## 48 80 2 5 2
## 49 80 0 7 2
## 50 80 2 5 5
## 51 80 0 34 3
## 52 80 0 8 3
## 53 80 1 4 2
## 54 80 1 18 2
## 55 80 1 17 1
## 56 80 1 21 3
## 57 80 0 10 2
## 58 80 1 9 3
## 59 80 1 12 2
## 60 80 1 10 2
## 61 80 0 21 2
## 62 80 1 6 2
## 63 80 0 1 2
## 64 80 0 22 2
## 65 80 0 10 5
## 66 80 0 6 3
## 67 80 0 33 0
## 68 80 0 8 2
## 69 80 2 3 3
## 70 80 0 18 2
## 71 80 0 10 5
## 72 80 0 20 3
## 73 80 2 9 4
## 74 80 1 16 2
## 75 80 1 12 2
## 76 80 0 5 2
## 77 80 0 15 2
## 78 80 0 10 2
## 79 80 0 12 2
## 80 80 1 8 2
## 81 80 2 10 1
## 82 80 1 16 2
## 83 80 2 17 2
## 84 80 2 14 5
## 85 80 1 12 4
## 86 80 1 24 3
## 87 80 0 13 4
## 88 80 0 33 2
## 89 80 0 26 2
## 90 80 0 6 2
## 91 80 1 9 2
## 92 80 1 9 2
## 93 80 0 6 5
## 94 80 0 28 1
## 95 80 1 5 2
## 96 80 1 7 6
## 97 80 1 19 2
## 98 80 1 10 3
## 99 80 0 7 1
## 100 80 0 1 3
## 101 80 0 16 3
## 102 80 0 12 2
## 103 80 1 7 3
## 104 80 3 21 5
## 105 80 1 1 2
## 106 80 0 8 2
## 107 80 1 9 3
## 108 80 1 8 2
## 109 80 0 7 3
## 110 80 0 23 3
## 111 80 1 5 0
## 112 80 0 17 5
## 113 80 2 9 2
## 114 80 1 9 3
## 115 80 1 29 2
## 116 80 1 7 5
## 117 80 1 12 2
## 118 80 3 10 3
## 119 80 3 1 4
## 120 80 0 38 1
## 121 80 2 16 3
## 122 80 0 28 4
## 123 80 1 27 2
## 124 80 1 17 3
## 125 80 1 17 2
## 126 80 1 28 3
## 127 80 0 1 6
## 128 80 1 28 2
## 129 80 1 24 4
## 130 80 0 4 6
## 131 80 1 14 4
## 132 80 1 24 1
## 133 80 0 25 2
## 134 80 0 7 3
## 135 80 1 24 4
## 136 80 1 10 6
## 137 80 1 17 2
## 138 80 1 4 3
## 139 80 3 3 4
## 140 80 0 6 3
## 141 80 1 20 2
## 142 80 1 24 4
## 143 80 0 18 2
## 144 80 0 8 2
## 145 80 3 10 4
## 146 80 0 8 5
## 147 80 0 16 3
## 148 80 0 1 3
## 149 80 0 5 3
## 150 80 1 36 3
## 151 80 1 2 2
## 152 80 0 19 4
## 153 80 1 22 3
## 154 80 0 9 3
## 155 80 0 5 2
## 156 80 1 4 3
## 157 80 3 5 2
## 158 80 0 9 2
## 159 80 0 0 6
## 160 80 1 10 3
## 161 80 1 18 3
## 162 80 2 8 2
## 163 80 0 8 2
## 164 80 2 10 2
## 165 80 1 17 1
## 166 80 0 6 4
## 167 80 1 31 5
## 168 80 1 6 0
## 169 80 3 20 3
## 170 80 0 5 3
## 171 80 0 6 3
## 172 80 0 10 4
## 173 80 0 9 2
## 174 80 0 2 3
## 175 80 1 5 2
## 176 80 1 10 3
## 177 80 1 7 2
## 178 80 1 1 5
## 179 80 1 26 2
## 180 80 1 23 2
## 181 80 1 5 2
## 182 80 2 6 3
## 183 80 1 5 2
## 184 80 1 14 5
## 185 80 1 25 3
## 186 80 0 18 2
## 187 80 0 21 2
## 188 80 0 2 2
## 189 80 1 33 2
## 190 80 0 9 6
## 191 80 3 11 4
## 192 80 1 11 3
## 193 80 0 16 2
## 194 80 1 8 2
## 195 80 0 7 1
## 196 80 0 4 2
## 197 80 3 11 2
## 198 80 1 6 2
## 199 80 1 33 2
## 200 80 2 9 6
## 201 80 1 21 2
## 202 80 1 5 6
## 203 80 3 7 1
## 204 80 1 17 2
## 205 80 3 27 2
## 206 80 1 7 0
## 207 80 0 7 1
## 208 80 0 13 2
## 209 80 0 8 5
## 210 80 1 32 3
## 211 80 0 7 2
## 212 80 2 12 3
## 213 80 0 17 2
## 214 80 1 9 3
## 215 80 1 8 5
## 216 80 1 12 4
## 217 80 2 5 2
## 218 80 2 10 3
## 219 80 2 4 2
## 220 80 0 18 0
## 221 80 3 10 2
## 222 80 2 2 2
## 223 80 0 22 3
## 224 80 0 23 5
## 225 80 2 4 2
## 226 80 0 10 2
## 227 80 0 9 3
## 228 80 2 10 2
## 229 80 3 6 2
## 230 80 1 18 2
## 231 80 1 1 2
## 232 80 0 5 3
## 233 80 2 26 4
## 234 80 0 6 2
## 235 80 0 10 4
## 236 80 0 7 0
## 237 80 0 7 3
## 238 80 1 9 3
## 239 80 0 37 2
## 240 80 0 37 2
## 241 80 0 4 2
## 242 80 0 12 3
## 243 80 1 29 2
## 244 80 0 15 3
## 245 80 1 20 2
## 246 80 1 6 2
## 247 80 1 7 2
## 248 80 1 3 2
## 249 80 0 10 2
## 250 80 0 4 3
## 251 80 0 5 0
## 252 80 2 21 3
## 253 80 3 23 2
## 254 80 3 0 6
## 255 80 1 23 3
## 256 80 0 20 2
## 257 80 1 9 1
## 258 80 0 10 3
## 259 80 0 8 0
## 260 80 1 22 3
## 261 80 2 31 4
## 262 80 1 10 5
## 263 80 0 10 2
## 264 80 2 12 3
## 265 80 1 5 5
## 266 80 1 8 3
## 267 80 0 11 2
## 268 80 2 13 4
## 269 80 0 11 2
## 270 80 1 16 3
## 271 80 2 20 4
## 272 80 2 1 2
## 273 80 0 10 2
## 274 80 0 1 0
## 275 80 0 1 3
## 276 80 0 11 2
## 277 80 0 7 2
## 278 80 1 10 1
## 279 80 1 10 2
## 280 80 0 1 5
## 281 80 1 6 2
## 282 80 1 10 5
## 283 80 1 13 4
## 284 80 1 18 4
## 285 80 0 7 4
## 286 80 0 5 2
## 287 80 1 10 1
## 288 80 0 4 2
## 289 80 1 19 0
## 290 80 0 3 3
## 291 80 0 10 2
## 292 80 2 6 3
## 293 80 1 6 3
## 294 80 0 17 3
## 295 80 0 1 5
## 296 80 0 16 6
## 297 80 0 12 0
## 298 80 0 1 2
## 299 80 1 25 2
## 300 80 1 8 2
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 2 10 9
## 2 3 5 4
## 3 2 1 1
## 4 3 5 4
## 5 3 10 7
## 6 2 13 12
## 7 2 9 8
## 8 3 7 7
## 9 3 10 9
## 10 3 1 0
## 11 3 1 1
## 12 3 1 1
## 13 2 9 8
## 14 3 4 3
## 15 3 0 0
## 16 3 20 9
## 17 3 5 3
## 18 2 7 7
## 19 2 9 7
## 20 4 9 8
## 21 3 6 2
## 22 2 5 4
## 23 3 5 2
## 24 3 2 2
## 25 3 7 7
## 26 2 10 3
## 27 3 3 2
## 28 3 3 2
## 29 1 4 2
## 30 3 3 2
## 31 4 5 4
## 32 3 10 7
## 33 3 8 7
## 34 4 5 4
## 35 2 1 0
## 36 2 2 1
## 37 4 20 4
## 38 3 4 2
## 39 3 21 9
## 40 4 1 0
## 41 3 3 2
## 42 3 2 2
## 43 3 5 0
## 44 2 9 7
## 45 2 1 0
## 46 3 5 4
## 47 1 5 2
## 48 3 5 2
## 49 1 6 5
## 50 3 5 3
## 51 4 34 6
## 52 3 8 7
## 53 3 3 0
## 54 2 7 7
## 55 4 15 11
## 56 3 21 8
## 57 2 10 9
## 58 1 5 3
## 59 3 7 7
## 60 2 3 2
## 61 3 20 15
## 62 4 5 2
## 63 3 1 0
## 64 2 21 9
## 65 4 5 2
## 66 3 3 2
## 67 3 12 9
## 68 4 5 4
## 69 3 2 2
## 70 3 1 0
## 71 3 1 1
## 72 3 20 11
## 73 3 5 3
## 74 3 5 3
## 75 2 5 2
## 76 2 3 2
## 77 3 3 2
## 78 3 9 7
## 79 2 11 10
## 80 3 5 2
## 81 3 8 7
## 82 2 15 1
## 83 2 1 0
## 84 4 10 9
## 85 3 10 7
## 86 2 5 3
## 87 3 12 6
## 88 4 33 7
## 89 3 5 2
## 90 2 4 3
## 91 3 8 7
## 92 3 9 8
## 93 2 6 2
## 94 2 8 3
## 95 3 5 4
## 96 3 2 1
## 97 4 1 0
## 98 3 10 0
## 99 2 6 2
## 100 4 1 0
## 101 3 1 0
## 102 3 11 8
## 103 4 7 7
## 104 2 5 3
## 105 4 1 0
## 106 3 8 7
## 107 3 3 2
## 108 3 5 2
## 109 3 3 2
## 110 3 5 3
## 111 2 4 2
## 112 3 16 6
## 113 3 6 2
## 114 1 7 7
## 115 2 1 0
## 116 3 2 2
## 117 3 11 9
## 118 2 10 4
## 119 3 1 0
## 120 2 37 10
## 121 3 15 13
## 122 3 5 4
## 123 3 1 0
## 124 3 5 2
## 125 2 15 7
## 126 3 2 0
## 127 3 1 0
## 128 3 27 10
## 129 2 4 2
## 130 3 3 2
## 131 1 10 9
## 132 4 20 6
## 133 3 7 1
## 134 2 2 2
## 135 3 1 0
## 136 2 7 7
## 137 2 16 8
## 138 3 1 0
## 139 3 3 2
## 140 3 2 2
## 141 1 17 9
## 142 2 24 7
## 143 2 4 2
## 144 3 5 4
## 145 3 10 8
## 146 3 8 7
## 147 3 1 0
## 148 3 1 0
## 149 3 5 3
## 150 3 36 6
## 151 2 2 2
## 152 3 2 2
## 153 2 22 3
## 154 3 7 7
## 155 2 4 2
## 156 3 4 2
## 157 3 5 4
## 158 3 5 4
## 159 3 0 0
## 160 3 4 0
## 161 3 1 0
## 162 2 8 6
## 163 3 4 2
## 164 3 7 7
## 165 2 5 3
## 166 3 3 2
## 167 2 29 10
## 168 3 5 4
## 169 3 20 8
## 170 3 5 4
## 171 2 4 3
## 172 4 3 2
## 173 1 7 6
## 174 3 2 2
## 175 3 5 2
## 176 3 10 7
## 177 3 4 2
## 178 2 1 0
## 179 4 2 2
## 180 3 12 11
## 181 3 2 2
## 182 2 5 3
## 183 2 3 2
## 184 3 5 4
## 185 3 25 10
## 186 4 14 7
## 187 3 21 6
## 188 2 2 2
## 189 1 5 4
## 190 3 8 7
## 191 2 11 8
## 192 3 10 8
## 193 3 16 15
## 194 3 2 2
## 195 3 7 4
## 196 3 3 2
## 197 3 3 2
## 198 3 5 4
## 199 3 33 9
## 200 2 4 3
## 201 4 18 16
## 202 4 5 3
## 203 3 3 2
## 204 3 7 7
## 205 2 3 2
## 206 3 4 2
## 207 3 7 5
## 208 2 11 7
## 209 4 3 2
## 210 2 7 0
## 211 3 6 4
## 212 3 10 9
## 213 3 15 7
## 214 2 5 3
## 215 3 5 2
## 216 3 11 10
## 217 2 1 0
## 218 2 10 2
## 219 2 4 2
## 220 3 11 9
## 221 3 10 9
## 222 3 2 2
## 223 3 22 17
## 224 3 20 18
## 225 1 2 2
## 226 3 8 7
## 227 3 9 6
## 228 4 10 2
## 229 2 4 3
## 230 2 18 7
## 231 3 1 0
## 232 3 5 3
## 233 2 25 9
## 234 3 5 4
## 235 3 10 7
## 236 3 2 2
## 237 3 1 1
## 238 3 7 7
## 239 3 36 10
## 240 3 10 9
## 241 4 3 2
## 242 3 7 7
## 243 2 8 1
## 244 3 5 4
## 245 3 20 7
## 246 3 6 5
## 247 1 2 2
## 248 3 3 2
## 249 3 10 8
## 250 3 3 2
## 251 3 2 2
## 252 3 21 16
## 253 2 21 6
## 254 2 0 0
## 255 3 12 9
## 256 3 4 3
## 257 3 3 2
## 258 3 8 4
## 259 3 8 7
## 260 3 19 7
## 261 4 7 7
## 262 2 10 8
## 263 3 10 8
## 264 3 6 3
## 265 2 4 2
## 266 3 5 4
## 267 3 4 3
## 268 4 7 7
## 269 4 1 0
## 270 2 16 10
## 271 2 4 2
## 272 3 0 0
## 273 3 9 4
## 274 3 1 0
## 275 4 1 0
## 276 3 3 2
## 277 3 7 7
## 278 3 10 9
## 279 1 4 3
## 280 3 0 0
## 281 2 4 1
## 282 3 10 7
## 283 3 9 4
## 284 3 5 4
## 285 1 5 3
## 286 3 5 4
## 287 2 7 7
## 288 2 4 3
## 289 1 1 0
## 290 3 2 2
## 291 2 5 2
## 292 2 5 3
## 293 2 6 4
## 294 2 9 6
## 295 3 1 0
## 296 3 11 8
## 297 3 11 8
## 298 2 1 0
## 299 3 24 0
## 300 3 7 6
## YearsSinceLastPromotion YearsWithCurrManager
## 1 6 8
## 2 1 4
## 3 0 0
## 4 1 2
## 5 0 4
## 6 12 8
## 7 0 0
## 8 7 7
## 9 1 8
## 10 0 0
## 11 0 0
## 12 1 0
## 13 1 7
## 14 0 3
## 15 0 0
## 16 3 7
## 17 0 2
## 18 1 7
## 19 7 3
## 20 7 7
## 21 4 1
## 22 1 3
## 23 1 4
## 24 2 1
## 25 0 7
## 26 9 7
## 27 1 2
## 28 1 2
## 29 1 3
## 30 1 2
## 31 1 4
## 32 0 9
## 33 0 7
## 34 0 3
## 35 1 0
## 36 2 2
## 37 4 8
## 38 0 3
## 39 13 3
## 40 0 0
## 41 2 2
## 42 2 2
## 43 0 2
## 44 5 4
## 45 0 0
## 46 4 3
## 47 1 4
## 48 0 3
## 49 1 3
## 50 1 3
## 51 1 16
## 52 3 0
## 53 0 2
## 54 0 7
## 55 5 9
## 56 1 6
## 57 7 8
## 58 1 4
## 59 1 7
## 60 0 2
## 61 1 12
## 62 0 3
## 63 0 0
## 64 13 14
## 65 0 0
## 66 0 2
## 67 3 8
## 68 1 2
## 69 2 1
## 70 0 0
## 71 0 0
## 72 13 17
## 73 1 4
## 74 0 4
## 75 2 2
## 76 1 2
## 77 1 2
## 78 0 7
## 79 0 7
## 80 0 4
## 81 7 7
## 82 0 9
## 83 0 0
## 84 1 8
## 85 0 8
## 86 0 2
## 87 2 11
## 88 15 12
## 89 0 0
## 90 1 2
## 91 6 7
## 92 5 8
## 93 1 4
## 94 0 7
## 95 1 2
## 96 2 2
## 97 0 0
## 98 7 9
## 99 1 5
## 100 0 0
## 101 0 0
## 102 5 8
## 103 5 6
## 104 1 3
## 105 0 0
## 106 1 3
## 107 0 2
## 108 1 4
## 109 0 2
## 110 4 4
## 111 3 2
## 112 0 13
## 113 1 3
## 114 1 7
## 115 0 0
## 116 2 2
## 117 5 7
## 118 1 9
## 119 0 0
## 120 1 8
## 121 5 8
## 122 0 4
## 123 0 0
## 124 0 3
## 125 6 13
## 126 2 2
## 127 0 0
## 128 15 7
## 129 1 2
## 130 1 2
## 131 9 8
## 132 14 17
## 133 0 7
## 134 2 2
## 135 1 0
## 136 6 2
## 137 4 11
## 138 0 0
## 139 1 2
## 140 2 2
## 141 0 15
## 142 14 9
## 143 3 3
## 144 1 4
## 145 7 7
## 146 7 7
## 147 0 0
## 148 0 0
## 149 0 2
## 150 2 13
## 151 0 2
## 152 2 2
## 153 11 11
## 154 0 2
## 155 2 3
## 156 3 2
## 157 0 0
## 158 1 0
## 159 0 0
## 160 2 3
## 161 0 0
## 162 1 7
## 163 1 3
## 164 7 7
## 165 1 3
## 166 1 2
## 167 11 10
## 168 1 4
## 169 3 8
## 170 0 3
## 171 0 1
## 172 1 2
## 173 0 7
## 174 2 1
## 175 0 4
## 176 1 7
## 177 0 2
## 178 0 1
## 179 0 1
## 180 11 11
## 181 2 0
## 182 1 4
## 183 0 2
## 184 0 4
## 185 3 9
## 186 8 10
## 187 8 6
## 188 1 2
## 189 1 4
## 190 1 7
## 191 2 7
## 192 0 7
## 193 1 10
## 194 2 2
## 195 0 6
## 196 2 2
## 197 0 2
## 198 1 4
## 199 0 10
## 200 2 3
## 201 0 11
## 202 0 3
## 203 1 2
## 204 7 7
## 205 0 2
## 206 0 2
## 207 0 7
## 208 1 7
## 209 1 2
## 210 0 6
## 211 0 4
## 212 9 8
## 213 4 12
## 214 1 3
## 215 1 2
## 216 5 7
## 217 0 0
## 218 6 7
## 219 0 2
## 220 0 9
## 221 1 2
## 222 2 2
## 223 11 15
## 224 15 15
## 225 2 2
## 226 0 0
## 227 1 1
## 228 0 2
## 229 0 2
## 230 12 17
## 231 0 0
## 232 0 3
## 233 14 13
## 234 4 3
## 235 0 8
## 236 2 2
## 237 0 0
## 238 0 7
## 239 4 13
## 240 7 7
## 241 1 2
## 242 0 7
## 243 7 7
## 244 0 1
## 245 2 13
## 246 1 2
## 247 2 2
## 248 0 2
## 249 4 7
## 250 2 2
## 251 2 2
## 252 5 10
## 253 12 6
## 254 0 0
## 255 4 9
## 256 1 3
## 257 0 2
## 258 1 7
## 259 7 4
## 260 11 16
## 261 0 0
## 262 9 6
## 263 6 0
## 264 0 1
## 265 1 3
## 266 0 3
## 267 1 2
## 268 5 7
## 269 0 0
## 270 10 1
## 271 0 3
## 272 0 0
## 273 1 7
## 274 0 0
## 275 0 0
## 276 1 2
## 277 0 7
## 278 0 9
## 279 0 2
## 280 0 0
## 281 0 3
## 282 2 8
## 283 7 0
## 284 0 3
## 285 0 0
## 286 1 2
## 287 1 0
## 288 1 2
## 289 0 0
## 290 2 2
## 291 2 3
## 292 4 3
## 293 1 1
## 294 0 8
## 295 0 0
## 296 3 9
## 297 5 7
## 298 0 1
## 299 1 7
## 300 7 7allData <- rbind.fill(fullTraining, ValidationNoAttrition)
allData## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## 7 7 41 No Travel_Rarely 1283 Research & Development
## 8 8 37 No Travel_Rarely 309 Sales
## 9 9 34 No Travel_Rarely 1333 Sales
## 10 10 34 No Travel_Frequently 653 Research & Development
## 11 11 43 No Travel_Rarely 823 Research & Development
## 12 12 28 No Non-Travel 280 Human Resources
## 13 13 35 No Travel_Rarely 950 Research & Development
## 14 14 30 No Travel_Rarely 202 Sales
## 15 15 46 No Travel_Rarely 991 Human Resources
## 16 16 31 No Non-Travel 1188 Sales
## 17 17 32 No Travel_Rarely 498 Research & Development
## 18 18 46 No Non-Travel 1144 Research & Development
## 19 19 34 No Travel_Rarely 181 Research & Development
## 20 20 44 No Travel_Rarely 170 Research & Development
## 21 21 36 No Travel_Rarely 913 Research & Development
## 22 22 48 No Travel_Rarely 817 Sales
## 23 23 43 No Travel_Frequently 1001 Research & Development
## 24 24 31 No Travel_Frequently 715 Sales
## 25 25 33 No Travel_Rarely 1069 Research & Development
## 26 26 44 No Travel_Rarely 1099 Sales
## 27 27 38 No Travel_Rarely 1245 Sales
## 28 28 33 Yes Travel_Rarely 603 Sales
## 29 29 53 Yes Travel_Rarely 1168 Sales
## 30 30 52 No Non-Travel 715 Research & Development
## 31 31 32 No Travel_Frequently 689 Sales
## 32 32 33 No Travel_Rarely 267 Research & Development
## 33 33 46 No Travel_Rarely 488 Sales
## 34 34 35 No Travel_Rarely 982 Research & Development
## 35 35 32 No Travel_Rarely 1373 Research & Development
## 36 36 32 No Travel_Frequently 1311 Research & Development
## 37 37 35 Yes Travel_Rarely 737 Sales
## 38 38 36 Yes Travel_Rarely 1456 Sales
## 39 39 56 No Travel_Rarely 206 Human Resources
## 40 40 31 No Travel_Frequently 793 Sales
## 41 41 58 No Non-Travel 350 Sales
## 42 42 45 No Travel_Rarely 954 Sales
## 43 43 49 No Travel_Rarely 464 Research & Development
## 44 44 42 No Travel_Rarely 1059 Research & Development
## 45 45 23 No Travel_Rarely 373 Research & Development
## 46 46 42 No Travel_Frequently 748 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 48 48 38 No Travel_Rarely 770 Sales
## 49 49 18 Yes Travel_Frequently 1306 Sales
## 50 50 27 No Non-Travel 691 Research & Development
## 51 51 38 No Travel_Rarely 362 Research & Development
## 52 52 58 No Travel_Rarely 1055 Research & Development
## 53 53 44 No Travel_Rarely 1117 Research & Development
## 54 54 45 No Non-Travel 805 Research & Development
## 55 55 31 No Travel_Rarely 746 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
## 57 57 42 Yes Travel_Frequently 933 Research & Development
## 58 58 29 No Travel_Rarely 1370 Research & Development
## 59 59 37 No Non-Travel 728 Research & Development
## 60 60 25 Yes Travel_Frequently 599 Sales
## 61 61 36 Yes Travel_Rarely 530 Sales
## 62 62 36 No Travel_Rarely 325 Research & Development
## 63 63 31 No Travel_Rarely 1154 Sales
## 64 64 18 No Non-Travel 1431 Research & Development
## 65 65 24 Yes Travel_Rarely 984 Research & Development
## 66 66 32 No Travel_Rarely 334 Research & Development
## 67 67 35 No Travel_Rarely 1017 Research & Development
## 68 68 23 No Travel_Rarely 571 Research & Development
## 69 69 35 No Travel_Frequently 664 Research & Development
## 70 70 37 No Travel_Rarely 1372 Research & Development
## 71 71 41 No Travel_Rarely 642 Research & Development
## 72 72 28 No Travel_Rarely 866 Sales
## 73 73 35 Yes Travel_Rarely 556 Research & Development
## 74 74 41 No Travel_Rarely 167 Research & Development
## 75 75 52 Yes Travel_Rarely 1030 Sales
## 76 76 30 No Travel_Frequently 721 Research & Development
## 77 77 34 No Travel_Rarely 971 Sales
## 78 78 46 No Travel_Rarely 563 Sales
## 79 79 45 No Non-Travel 1052 Sales
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 81 81 37 Yes Travel_Rarely 1141 Research & Development
## 82 82 35 No Travel_Rarely 1370 Research & Development
## 83 83 38 No Travel_Frequently 508 Research & Development
## 84 84 37 No Travel_Rarely 1319 Research & Development
## 85 85 36 No Travel_Rarely 429 Research & Development
## 86 86 59 No Travel_Rarely 715 Research & Development
## 87 87 47 No Travel_Rarely 703 Sales
## 88 88 35 Yes Travel_Rarely 622 Research & Development
## 89 89 27 No Travel_Frequently 994 Sales
## 90 90 46 No Travel_Rarely 705 Sales
## 91 91 38 No Travel_Frequently 1490 Research & Development
## 92 92 29 Yes Travel_Rarely 408 Sales
## 93 93 37 Yes Travel_Rarely 1373 Research & Development
## 94 94 35 No Travel_Rarely 670 Research & Development
## 95 95 50 No Travel_Rarely 1452 Research & Development
## 96 96 42 No Travel_Rarely 635 Sales
## 97 97 46 No Travel_Rarely 228 Sales
## 98 98 37 No Non-Travel 1040 Research & Development
## 99 99 29 No Travel_Frequently 410 Research & Development
## 100 100 34 No Travel_Rarely 401 Research & Development
## 101 101 27 No Travel_Rarely 1377 Research & Development
## 102 102 31 No Travel_Rarely 326 Sales
## 103 103 34 No Non-Travel 1381 Sales
## 104 104 23 No Travel_Rarely 885 Research & Development
## 105 105 53 No Travel_Rarely 447 Research & Development
## 106 106 26 No Travel_Rarely 1384 Research & Development
## 107 107 29 No Non-Travel 1496 Research & Development
## 108 108 55 No Travel_Rarely 189 Human Resources
## 109 109 46 Yes Travel_Rarely 261 Research & Development
## 110 110 32 No Travel_Rarely 529 Research & Development
## 111 111 28 No Travel_Frequently 773 Research & Development
## 112 112 52 No Travel_Rarely 1053 Research & Development
## 113 113 52 No Travel_Rarely 994 Research & Development
## 114 114 35 No Non-Travel 1225 Research & Development
## 115 115 35 No Travel_Rarely 1361 Sales
## 116 116 37 No Non-Travel 1252 Sales
## 117 117 25 No Travel_Rarely 1356 Sales
## 118 118 29 No Travel_Rarely 1378 Research & Development
## 119 119 41 No Travel_Rarely 465 Research & Development
## 120 120 34 No Travel_Rarely 404 Research & Development
## 121 121 36 No Non-Travel 427 Research & Development
## 122 122 21 No Travel_Rarely 501 Sales
## 123 123 45 No Travel_Rarely 930 Sales
## 124 124 38 No Travel_Rarely 243 Sales
## 125 125 29 No Travel_Frequently 442 Sales
## 126 126 35 No Travel_Rarely 1224 Sales
## 127 127 32 Yes Travel_Frequently 1125 Research & Development
## 128 128 45 No Travel_Rarely 556 Research & Development
## 129 129 43 No Travel_Rarely 930 Research & Development
## 130 130 46 No Travel_Rarely 1319 Sales
## 131 131 29 No Travel_Rarely 991 Sales
## 132 132 49 No Travel_Rarely 1313 Sales
## 133 133 42 No Travel_Frequently 1368 Research & Development
## 134 134 39 No Travel_Rarely 492 Research & Development
## 135 135 43 No Travel_Frequently 559 Research & Development
## 136 136 40 No Travel_Frequently 791 Research & Development
## 137 137 29 Yes Travel_Rarely 805 Research & Development
## 138 138 54 No Travel_Rarely 1441 Research & Development
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 140 140 37 No Travel_Rarely 1189 Sales
## 141 141 56 No Travel_Rarely 1369 Research & Development
## 142 142 58 No Travel_Rarely 848 Research & Development
## 143 143 46 No Travel_Rarely 566 Research & Development
## 144 144 37 No Travel_Rarely 228 Sales
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 146 146 29 No Non-Travel 746 Sales
## 147 147 33 No Travel_Rarely 931 Research & Development
## 148 148 33 No Travel_Rarely 1242 Sales
## 149 149 24 No Travel_Rarely 477 Research & Development
## 150 150 28 No Travel_Rarely 580 Research & Development
## 151 151 31 Yes Travel_Rarely 1365 Sales
## 152 152 32 No Travel_Rarely 929 Sales
## 153 153 32 No Travel_Rarely 906 Sales
## 154 154 41 No Travel_Rarely 447 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 156 156 30 No Non-Travel 641 Sales
## 157 157 40 No Travel_Rarely 444 Sales
## 158 158 39 Yes Travel_Frequently 203 Research & Development
## 159 159 43 No Travel_Frequently 1082 Research & Development
## 160 160 42 No Travel_Frequently 458 Research & Development
## 161 161 30 No Travel_Rarely 852 Research & Development
## 162 162 28 Yes Non-Travel 1366 Research & Development
## 163 163 33 No Travel_Rarely 1216 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 165 165 35 No Travel_Rarely 1232 Sales
## 166 166 20 Yes Travel_Rarely 500 Sales
## 167 167 49 No Travel_Rarely 301 Research & Development
## 168 168 33 No Travel_Rarely 922 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 170 170 29 No Travel_Rarely 1107 Research & Development
## 171 171 30 No Travel_Rarely 501 Sales
## 172 172 47 No Non-Travel 1169 Research & Development
## 173 173 46 No Travel_Frequently 1034 Research & Development
## 174 174 33 No Travel_Rarely 536 Sales
## 175 175 53 No Travel_Rarely 1376 Sales
## 176 176 21 No Travel_Rarely 984 Research & Development
## 177 177 55 Yes Travel_Rarely 725 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 179 179 32 No Travel_Frequently 967 Sales
## 180 180 38 No Travel_Rarely 702 Sales
## 181 181 35 No Travel_Rarely 819 Research & Development
## 182 182 38 No Travel_Rarely 1009 Sales
## 183 183 43 No Travel_Frequently 957 Research & Development
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 185 185 39 No Travel_Frequently 443 Research & Development
## 186 186 28 No Travel_Rarely 1158 Research & Development
## 187 187 25 No Travel_Frequently 772 Research & Development
## 188 188 47 No Travel_Rarely 1225 Sales
## 189 189 50 No Travel_Rarely 797 Research & Development
## 190 190 28 No Non-Travel 120 Sales
## 191 191 30 No Travel_Rarely 1275 Research & Development
## 192 192 31 No Travel_Frequently 1125 Sales
## 193 193 46 No Travel_Rarely 1485 Research & Development
## 194 194 48 No Travel_Rarely 163 Sales
## 195 195 26 No Travel_Rarely 683 Research & Development
## 196 196 51 No Travel_Rarely 833 Research & Development
## 197 197 51 No Travel_Rarely 432 Research & Development
## 198 198 49 No Travel_Rarely 174 Sales
## 199 199 44 No Travel_Rarely 477 Research & Development
## 200 200 24 No Travel_Rarely 823 Research & Development
## 201 201 40 Yes Non-Travel 1479 Sales
## 202 202 32 No Travel_Rarely 548 Research & Development
## 203 203 32 No Travel_Rarely 859 Research & Development
## 204 204 28 Yes Travel_Rarely 654 Research & Development
## 205 205 32 No Travel_Rarely 1401 Sales
## 206 206 44 No Travel_Rarely 661 Research & Development
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 208 208 38 Yes Travel_Rarely 1180 Research & Development
## 209 209 30 No Travel_Rarely 201 Research & Development
## 210 210 41 No Travel_Rarely 802 Sales
## 211 211 41 No Travel_Rarely 582 Research & Development
## 212 212 42 No Travel_Rarely 265 Sales
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 214 214 42 No Travel_Rarely 932 Research & Development
## 215 215 27 No Travel_Rarely 1115 Research & Development
## 216 216 40 No Travel_Rarely 896 Research & Development
## 217 217 38 No Travel_Rarely 1495 Research & Development
## 218 218 48 No Travel_Frequently 117 Research & Development
## 219 219 30 No Travel_Rarely 438 Research & Development
## 220 220 26 No Travel_Rarely 583 Research & Development
## 221 221 34 No Travel_Rarely 304 Sales
## 222 222 45 Yes Travel_Frequently 306 Sales
## 223 223 33 No Non-Travel 530 Sales
## 224 224 28 No Travel_Rarely 1423 Research & Development
## 225 225 38 No Travel_Rarely 1206 Research & Development
## 226 226 30 No Travel_Rarely 911 Research & Development
## 227 227 35 No Travel_Rarely 464 Research & Development
## 228 228 22 No Travel_Rarely 1136 Research & Development
## 229 229 24 No Travel_Rarely 1353 Sales
## 230 230 49 No Travel_Rarely 465 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 232 232 35 No Travel_Rarely 890 Sales
## 233 233 47 Yes Travel_Frequently 1093 Sales
## 234 234 54 No Travel_Rarely 1147 Sales
## 235 235 43 No Travel_Frequently 394 Sales
## 236 236 37 Yes Travel_Rarely 625 Sales
## 237 237 42 No Travel_Rarely 300 Research & Development
## 238 238 32 No Travel_Rarely 646 Research & Development
## 239 239 25 No Travel_Rarely 309 Human Resources
## 240 240 50 No Travel_Rarely 813 Research & Development
## 241 241 42 No Travel_Rarely 319 Research & Development
## 242 242 45 No Travel_Frequently 364 Research & Development
## 243 243 29 No Travel_Rarely 1328 Research & Development
## 244 244 25 Yes Travel_Rarely 383 Sales
## 245 245 29 No Travel_Rarely 718 Research & Development
## 246 246 29 No Travel_Rarely 1086 Research & Development
## 247 247 31 No Travel_Rarely 1232 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 249 249 41 No Travel_Frequently 1200 Research & Development
## 250 250 41 No Travel_Rarely 1276 Sales
## 251 251 33 No Non-Travel 1313 Research & Development
## 252 252 36 No Non-Travel 1229 Sales
## 253 253 45 No Travel_Rarely 252 Research & Development
## 254 254 31 Yes Travel_Rarely 359 Human Resources
## 255 255 40 No Non-Travel 218 Research & Development
## 256 256 43 No Non-Travel 343 Research & Development
## 257 257 31 No Travel_Frequently 444 Sales
## 258 258 30 No Travel_Rarely 855 Sales
## 259 259 34 No Travel_Rarely 1480 Sales
## 260 260 25 No Travel_Rarely 977 Research & Development
## 261 261 31 Yes Travel_Frequently 703 Sales
## 262 262 29 Yes Travel_Rarely 224 Research & Development
## 263 263 33 Yes Travel_Rarely 813 Research & Development
## 264 264 29 No Travel_Frequently 1404 Sales
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 266 266 35 No Travel_Rarely 809 Research & Development
## 267 267 34 No Travel_Frequently 829 Research & Development
## 268 268 32 Yes Travel_Rarely 1259 Research & Development
## 269 269 33 No Travel_Frequently 1303 Research & Development
## 270 270 30 No Travel_Rarely 1176 Research & Development
## 271 271 45 No Non-Travel 589 Sales
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 273 273 27 No Travel_Rarely 728 Sales
## 274 274 36 No Travel_Rarely 329 Sales
## 275 275 55 No Travel_Rarely 692 Research & Development
## 276 276 56 No Travel_Rarely 718 Research & Development
## 277 277 22 No Travel_Rarely 253 Research & Development
## 278 278 43 No Travel_Frequently 185 Research & Development
## 279 279 25 Yes Travel_Rarely 240 Sales
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 281 281 43 No Travel_Rarely 1179 Sales
## 282 282 41 No Travel_Rarely 263 Research & Development
## 283 283 40 No Travel_Rarely 1194 Research & Development
## 284 284 31 No Travel_Rarely 691 Sales
## 285 285 38 No Travel_Rarely 433 Human Resources
## 286 286 33 No Travel_Rarely 589 Research & Development
## 287 287 22 Yes Travel_Rarely 391 Research & Development
## 288 288 34 No Travel_Rarely 1346 Research & Development
## 289 289 36 No Travel_Frequently 1467 Sales
## 290 290 35 No Travel_Rarely 384 Sales
## 291 291 45 No Travel_Rarely 974 Research & Development
## 292 292 36 No Non-Travel 1105 Research & Development
## 293 293 27 No Travel_Rarely 894 Research & Development
## 294 294 47 No Non-Travel 1162 Research & Development
## 295 295 40 No Travel_Rarely 898 Human Resources
## 296 296 35 No Travel_Rarely 1490 Research & Development
## 297 297 34 No Travel_Frequently 1069 Research & Development
## 298 298 34 Yes Travel_Frequently 658 Research & Development
## 299 299 31 Yes Non-Travel 335 Research & Development
## 300 300 28 Yes Travel_Rarely 1157 Research & Development
## 301 301 32 No Travel_Frequently 379 Sales
## 302 302 35 No Travel_Frequently 853 Sales
## 303 303 55 No Travel_Rarely 1441 Research & Development
## 304 304 35 No Travel_Rarely 1395 Research & Development
## 305 305 58 Yes Travel_Rarely 289 Research & Development
## 306 306 24 No Non-Travel 1269 Research & Development
## 307 307 50 No Travel_Rarely 804 Research & Development
## 308 308 34 No Non-Travel 697 Research & Development
## 309 309 55 No Travel_Frequently 135 Research & Development
## 310 310 25 No Travel_Rarely 810 Sales
## 311 311 34 No Travel_Rarely 1351 Research & Development
## 312 312 40 No Travel_Rarely 302 Research & Development
## 313 313 31 Yes Travel_Frequently 561 Research & Development
## 314 314 51 No Travel_Rarely 1169 Research & Development
## 315 315 60 No Travel_Rarely 1179 Sales
## 316 316 47 No Travel_Rarely 955 Sales
## 317 317 34 No Travel_Frequently 303 Sales
## 318 318 24 No Travel_Rarely 771 Research & Development
## 319 319 39 No Travel_Rarely 1498 Sales
## 320 320 19 No Travel_Rarely 265 Research & Development
## 321 321 32 No Non-Travel 1184 Research & Development
## 322 322 34 No Travel_Rarely 1397 Research & Development
## 323 323 30 No Travel_Frequently 1012 Research & Development
## 324 324 46 No Travel_Rarely 1003 Research & Development
## 325 325 39 No Travel_Rarely 466 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 327 327 44 No Travel_Rarely 1313 Research & Development
## 328 328 42 No Travel_Rarely 557 Research & Development
## 329 329 38 No Travel_Frequently 1394 Research & Development
## 330 330 42 No Travel_Rarely 1147 Human Resources
## 331 331 49 No Travel_Rarely 809 Research & Development
## 332 332 32 Yes Travel_Rarely 1045 Sales
## 333 333 44 Yes Travel_Frequently 429 Research & Development
## 334 334 27 No Travel_Rarely 608 Research & Development
## 335 335 59 No Non-Travel 1420 Human Resources
## 336 336 33 Yes Travel_Frequently 827 Research & Development
## 337 337 35 No Travel_Frequently 1182 Sales
## 338 338 34 No Travel_Frequently 618 Research & Development
## 339 339 50 Yes Travel_Frequently 959 Sales
## 340 340 27 No Travel_Rarely 1240 Research & Development
## 341 341 36 No Travel_Rarely 852 Research & Development
## 342 342 28 No Travel_Frequently 193 Research & Development
## 343 343 33 No Travel_Rarely 654 Research & Development
## 344 344 30 No Travel_Frequently 160 Research & Development
## 345 345 30 No Travel_Rarely 1329 Sales
## 346 346 39 No Travel_Rarely 119 Sales
## 347 347 34 No Travel_Frequently 1003 Research & Development
## 348 348 26 No Travel_Frequently 1479 Research & Development
## 349 349 44 No Non-Travel 111 Research & Development
## 350 350 56 No Travel_Rarely 832 Research & Development
## 351 351 42 No Travel_Rarely 1396 Research & Development
## 352 352 31 No Travel_Rarely 218 Sales
## 353 353 40 No Travel_Frequently 902 Research & Development
## 354 354 40 No Non-Travel 1142 Research & Development
## 355 355 41 No Travel_Rarely 796 Sales
## 356 356 34 No Travel_Rarely 1326 Sales
## 357 357 26 No Travel_Rarely 841 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
## 359 359 32 No Travel_Rarely 427 Research & Development
## 360 360 36 No Travel_Rarely 1403 Research & Development
## 361 361 52 No Travel_Rarely 699 Research & Development
## 362 362 29 Yes Travel_Frequently 746 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 364 364 40 No Travel_Rarely 1416 Research & Development
## 365 365 55 No Travel_Rarely 282 Research & Development
## 366 366 56 No Travel_Frequently 906 Sales
## 367 367 34 No Travel_Frequently 135 Research & Development
## 368 368 40 No Travel_Rarely 1342 Sales
## 369 369 23 No Travel_Rarely 977 Research & Development
## 370 370 32 No Travel_Rarely 1018 Research & Development
## 371 371 31 No Travel_Frequently 853 Research & Development
## 372 372 30 No Non-Travel 1398 Sales
## 373 373 45 No Travel_Frequently 1249 Research & Development
## 374 374 37 No Travel_Rarely 1470 Research & Development
## 375 375 20 No Travel_Rarely 727 Sales
## 376 376 50 No Travel_Frequently 333 Research & Development
## 377 377 36 Yes Travel_Rarely 885 Research & Development
## 378 378 42 No Travel_Rarely 419 Sales
## 379 379 29 No Travel_Rarely 942 Research & Development
## 380 380 30 Yes Travel_Rarely 138 Research & Development
## 381 381 47 No Travel_Rarely 1482 Research & Development
## 382 382 26 No Travel_Rarely 1355 Human Resources
## 383 383 48 No Travel_Rarely 1221 Sales
## 384 384 23 No Travel_Rarely 541 Sales
## 385 385 51 Yes Travel_Rarely 1323 Research & Development
## 386 386 38 No Travel_Rarely 1333 Research & Development
## 387 387 35 No Travel_Frequently 944 Sales
## 388 388 29 No Travel_Rarely 232 Research & Development
## 389 389 36 No Travel_Rarely 530 Sales
## 390 390 21 Yes Travel_Frequently 756 Sales
## 391 391 34 No Travel_Rarely 1111 Sales
## 392 392 26 No Travel_Rarely 1443 Sales
## 393 393 31 No Non-Travel 976 Research & Development
## 394 394 60 No Travel_Frequently 1499 Sales
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 396 396 31 No Travel_Rarely 192 Research & Development
## 397 397 46 No Travel_Rarely 1009 Research & Development
## 398 398 34 Yes Travel_Rarely 1107 Human Resources
## 399 399 56 No Non-Travel 667 Research & Development
## 400 400 44 No Travel_Frequently 1193 Research & Development
## 401 401 30 No Travel_Rarely 317 Research & Development
## 402 402 42 No Non-Travel 926 Research & Development
## 403 403 47 No Travel_Rarely 207 Research & Development
## 404 404 36 No Travel_Rarely 711 Research & Development
## 405 405 35 No Travel_Rarely 1343 Research & Development
## 406 406 43 No Travel_Rarely 782 Research & Development
## 407 407 31 No Travel_Rarely 196 Sales
## 408 408 36 No Travel_Rarely 917 Research & Development
## 409 409 53 No Travel_Rarely 1219 Sales
## 410 410 43 No Travel_Rarely 589 Research & Development
## 411 411 36 No Travel_Rarely 1278 Human Resources
## 412 412 41 No Travel_Rarely 918 Sales
## 413 413 37 No Travel_Rarely 408 Research & Development
## 414 414 32 No Travel_Frequently 1318 Sales
## 415 415 34 Yes Travel_Frequently 296 Sales
## 416 416 35 No Non-Travel 1180 Research & Development
## 417 417 30 No Non-Travel 1116 Research & Development
## 418 418 36 No Travel_Frequently 566 Research & Development
## 419 419 37 No Travel_Rarely 977 Research & Development
## 420 420 29 No Travel_Rarely 986 Research & Development
## 421 421 34 No Travel_Rarely 1153 Research & Development
## 422 422 37 No Non-Travel 1063 Research & Development
## 423 423 31 Yes Travel_Rarely 249 Sales
## 424 424 18 No Non-Travel 1124 Research & Development
## 425 425 28 No Travel_Rarely 736 Sales
## 426 426 43 No Travel_Rarely 415 Sales
## 427 427 30 No Non-Travel 990 Research & Development
## 428 428 21 No Travel_Rarely 996 Research & Development
## 429 429 32 Yes Travel_Rarely 515 Research & Development
## 430 430 34 No Travel_Rarely 470 Research & Development
## 431 431 27 No Travel_Rarely 1377 Sales
## 432 432 40 No Non-Travel 458 Research & Development
## 433 433 49 Yes Travel_Frequently 1475 Research & Development
## 434 434 29 No Travel_Rarely 590 Research & Development
## 435 435 40 No Travel_Rarely 658 Sales
## 436 436 27 No Travel_Rarely 1130 Sales
## 437 437 28 Yes Travel_Rarely 890 Research & Development
## 438 438 26 No Travel_Rarely 157 Research & Development
## 439 439 50 Yes Travel_Rarely 869 Sales
## 440 440 38 No Travel_Rarely 827 Research & Development
## 441 441 32 No Travel_Frequently 1316 Research & Development
## 442 442 29 No Travel_Rarely 1082 Research & Development
## 443 443 28 Yes Travel_Rarely 529 Research & Development
## 444 444 44 No Travel_Rarely 528 Human Resources
## 445 445 46 No Travel_Rarely 734 Research & Development
## 446 446 47 No Travel_Rarely 465 Research & Development
## 447 447 38 No Travel_Frequently 1189 Research & Development
## 448 448 42 No Travel_Rarely 1128 Research & Development
## 449 449 38 No Travel_Frequently 653 Research & Development
## 450 450 45 No Non-Travel 336 Sales
## 451 451 33 No Travel_Rarely 501 Research & Development
## 452 452 44 No Travel_Rarely 1467 Research & Development
## 453 453 35 Yes Travel_Rarely 303 Sales
## 454 454 33 No Travel_Rarely 217 Sales
## 455 455 48 No Travel_Rarely 1236 Research & Development
## 456 456 40 No Travel_Rarely 611 Sales
## 457 457 31 No Travel_Rarely 1112 Sales
## 458 458 51 No Travel_Rarely 632 Sales
## 459 459 56 No Travel_Rarely 1255 Research & Development
## 460 460 36 No Travel_Rarely 172 Research & Development
## 461 461 42 No Travel_Frequently 1474 Research & Development
## 462 462 36 No Travel_Rarely 311 Research & Development
## 463 463 50 No Travel_Frequently 809 Sales
## 464 464 31 No Travel_Frequently 163 Research & Development
## 465 465 43 Yes Travel_Frequently 807 Research & Development
## 466 466 38 No Travel_Rarely 371 Research & Development
## 467 467 36 No Travel_Rarely 1157 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 469 469 26 Yes Non-Travel 265 Sales
## 470 470 53 No Travel_Rarely 1084 Research & Development
## 471 471 27 No Travel_Rarely 1354 Research & Development
## 472 472 29 Yes Travel_Frequently 459 Research & Development
## 473 473 45 No Travel_Rarely 193 Research & Development
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 475 475 28 No Travel_Rarely 895 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 477 477 37 No Travel_Rarely 342 Sales
## 478 478 26 No Travel_Rarely 775 Sales
## 479 479 52 No Travel_Rarely 258 Research & Development
## 480 480 37 No Travel_Rarely 161 Research & Development
## 481 481 29 No Travel_Rarely 136 Research & Development
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 483 483 34 No Travel_Frequently 735 Research & Development
## 484 484 56 No Travel_Rarely 713 Research & Development
## 485 485 58 Yes Travel_Rarely 147 Research & Development
## 486 486 41 Yes Travel_Rarely 1102 Sales
## 487 487 30 Yes Travel_Rarely 945 Sales
## 488 488 31 No Travel_Rarely 670 Research & Development
## 489 489 48 Yes Travel_Rarely 626 Research & Development
## 490 490 34 No Travel_Rarely 970 Research & Development
## 491 491 49 No Travel_Frequently 636 Research & Development
## 492 492 50 No Travel_Rarely 1322 Research & Development
## 493 493 29 No Travel_Rarely 469 Sales
## 494 494 35 No Travel_Rarely 1029 Research & Development
## 495 495 36 No Travel_Rarely 1266 Sales
## 496 496 51 No Travel_Rarely 1318 Sales
## 497 497 45 No Travel_Rarely 192 Research & Development
## 498 498 37 No Travel_Frequently 889 Research & Development
## 499 499 36 No Travel_Rarely 1299 Research & Development
## 500 500 39 No Non-Travel 1251 Sales
## 501 501 58 Yes Travel_Rarely 601 Research & Development
## 502 502 28 Yes Travel_Rarely 103 Research & Development
## 503 503 48 Yes Travel_Frequently 708 Sales
## 504 504 29 No Travel_Rarely 1389 Research & Development
## 505 505 47 No Travel_Rarely 359 Research & Development
## 506 506 32 No Travel_Rarely 824 Research & Development
## 507 507 33 Yes Travel_Rarely 350 Sales
## 508 508 51 No Travel_Frequently 541 Sales
## 509 509 37 No Travel_Frequently 319 Sales
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
## 511 511 38 No Travel_Rarely 345 Sales
## 512 512 39 No Non-Travel 1485 Research & Development
## 513 513 32 No Travel_Frequently 585 Research & Development
## 514 514 33 No Travel_Frequently 515 Research & Development
## 515 515 21 No Non-Travel 895 Sales
## 516 516 51 No Travel_Rarely 1302 Research & Development
## 517 517 25 Yes Travel_Rarely 867 Sales
## 518 518 42 No Travel_Rarely 855 Research & Development
## 519 519 30 No Travel_Rarely 1138 Research & Development
## 520 520 56 Yes Travel_Rarely 1162 Research & Development
## 521 521 34 No Travel_Frequently 702 Research & Development
## 522 522 28 Yes Travel_Rarely 329 Research & Development
## 523 523 45 No Travel_Rarely 1339 Research & Development
## 524 524 49 No Travel_Rarely 1245 Research & Development
## 525 525 59 No Travel_Rarely 142 Research & Development
## 526 526 53 No Travel_Frequently 124 Sales
## 527 527 45 No Travel_Rarely 1268 Sales
## 528 528 31 No Travel_Rarely 1332 Research & Development
## 529 529 35 No Travel_Rarely 1214 Research & Development
## 530 530 56 Yes Travel_Rarely 441 Research & Development
## 531 531 38 No Travel_Rarely 330 Research & Development
## 532 532 36 No Travel_Rarely 884 Sales
## 533 533 29 No Travel_Rarely 1252 Research & Development
## 534 534 32 No Non-Travel 1146 Research & Development
## 535 535 29 Yes Travel_Rarely 318 Research & Development
## 536 536 43 No Non-Travel 1344 Research & Development
## 537 537 38 No Travel_Rarely 130 Sales
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## 539 539 24 Yes Travel_Rarely 1448 Sales
## 540 540 42 No Travel_Rarely 647 Sales
## 541 541 30 No Travel_Rarely 853 Research & Development
## 542 542 33 No Travel_Rarely 867 Research & Development
## 543 543 27 No Non-Travel 1450 Research & Development
## 544 544 38 No Travel_Rarely 1261 Research & Development
## 545 545 34 No Travel_Rarely 121 Research & Development
## 546 546 34 No Travel_Rarely 678 Research & Development
## 547 547 49 No Travel_Rarely 527 Research & Development
## 548 548 40 No Travel_Frequently 720 Research & Development
## 549 549 38 No Travel_Rarely 849 Research & Development
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## 555 555 40 No Non-Travel 1151 Research & Development
## 556 556 37 No Travel_Rarely 558 Sales
## 557 557 54 No Travel_Rarely 548 Research & Development
## 558 558 34 No Travel_Rarely 254 Research & Development
## 559 559 42 No Travel_Rarely 933 Research & Development
## 560 560 26 No Travel_Rarely 933 Sales
## 561 561 36 No Travel_Frequently 1480 Research & Development
## 562 562 58 No Travel_Frequently 1216 Research & Development
## 563 563 27 No Travel_Rarely 1134 Research & Development
## 564 564 31 No Non-Travel 697 Research & Development
## 565 565 30 No Travel_Rarely 921 Research & Development
## 566 566 44 No Travel_Rarely 1037 Research & Development
## 567 567 32 No Travel_Rarely 128 Research & Development
## 568 568 30 Yes Travel_Rarely 740 Sales
## 569 569 42 No Travel_Rarely 462 Sales
## 570 570 50 No Travel_Rarely 939 Research & Development
## 571 571 41 No Travel_Rarely 337 Sales
## 572 572 21 Yes Travel_Rarely 1427 Research & Development
## 573 573 28 No Travel_Frequently 783 Sales
## 574 574 34 No Travel_Rarely 810 Sales
## 575 575 26 No Travel_Rarely 703 Sales
## 576 576 37 No Travel_Rarely 1115 Research & Development
## 577 577 38 No Travel_Rarely 1404 Sales
## 578 578 44 No Non-Travel 981 Research & Development
## 579 579 39 No Travel_Frequently 711 Research & Development
## 580 580 29 No Travel_Rarely 1329 Research & Development
## 581 581 25 No Travel_Rarely 882 Research & Development
## 582 582 42 No Travel_Rarely 810 Research & Development
## 583 583 38 No Travel_Rarely 833 Research & Development
## 584 584 28 No Travel_Rarely 760 Sales
## 585 585 44 No Travel_Rarely 1459 Research & Development
## 586 586 40 No Travel_Rarely 905 Research & Development
## 587 587 38 No Non-Travel 573 Research & Development
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## 589 589 37 No Travel_Rarely 1462 Research & Development
## 590 590 35 No Travel_Rarely 1142 Research & Development
## 591 591 22 No Non-Travel 457 Research & Development
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## 593 593 28 Yes Travel_Frequently 1496 Sales
## 594 594 49 No Travel_Rarely 722 Research & Development
## 595 595 40 No Travel_Rarely 884 Research & Development
## 596 596 33 No Travel_Rarely 586 Sales
## 597 597 35 No Travel_Rarely 672 Research & Development
## 598 598 40 No Travel_Rarely 555 Research & Development
## 599 599 27 No Travel_Rarely 155 Research & Development
## 600 600 24 Yes Travel_Rarely 813 Research & Development
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## 602 602 32 No Non-Travel 1200 Research & Development
## 603 603 30 No Travel_Rarely 153 Research & Development
## 604 604 29 No Travel_Rarely 1401 Research & Development
## 605 605 29 Yes Travel_Rarely 428 Sales
## 606 606 53 No Travel_Rarely 970 Research & Development
## 607 607 59 No Travel_Frequently 1225 Sales
## 608 608 38 No Travel_Rarely 322 Sales
## 609 609 26 No Travel_Rarely 652 Research & Development
## 610 610 33 No Non-Travel 1038 Sales
## 611 611 35 No Travel_Rarely 817 Research & Development
## 612 612 41 No Non-Travel 509 Research & Development
## 613 613 33 Yes Travel_Rarely 465 Research & Development
## 614 614 19 Yes Travel_Rarely 419 Sales
## 615 615 30 No Travel_Rarely 231 Sales
## 616 616 34 No Travel_Rarely 1157 Research & Development
## 617 617 33 No Travel_Rarely 1099 Research & Development
## 618 618 44 No Travel_Rarely 1488 Sales
## 619 619 39 No Travel_Rarely 613 Research & Development
## 620 620 35 No Non-Travel 727 Research & Development
## 621 621 20 Yes Travel_Frequently 871 Research & Development
## 622 622 47 No Travel_Frequently 1379 Research & Development
## 623 623 34 No Travel_Rarely 182 Research & Development
## 624 624 35 No Travel_Rarely 1146 Human Resources
## 625 625 28 Yes Travel_Frequently 289 Research & Development
## 626 626 27 No Travel_Rarely 1167 Research & Development
## 627 627 24 No Travel_Rarely 1476 Sales
## 628 628 38 No Travel_Rarely 343 Research & Development
## 629 629 55 No Travel_Frequently 1091 Research & Development
## 630 630 34 No Travel_Rarely 1442 Research & Development
## 631 631 55 No Travel_Rarely 836 Research & Development
## 632 632 34 No Travel_Rarely 937 Sales
## 633 633 38 No Travel_Rarely 1035 Sales
## 634 634 36 No Travel_Rarely 922 Research & Development
## 635 635 43 No Travel_Rarely 1001 Research & Development
## 636 636 35 No Travel_Frequently 636 Research & Development
## 637 637 42 No Travel_Rarely 469 Research & Development
## 638 638 34 No Travel_Rarely 1239 Sales
## 639 639 24 No Travel_Rarely 691 Research & Development
## 640 640 27 Yes Travel_Rarely 1420 Sales
## 641 641 30 No Travel_Rarely 1358 Research & Development
## 642 642 40 No Travel_Rarely 750 Research & Development
## 643 643 55 No Non-Travel 444 Research & Development
## 644 644 34 Yes Non-Travel 967 Research & Development
## 645 645 41 Yes Non-Travel 906 Research & Development
## 646 646 40 No Travel_Rarely 630 Sales
## 647 647 18 Yes Non-Travel 247 Research & Development
## 648 648 26 No Travel_Rarely 390 Research & Development
## 649 649 35 No Travel_Rarely 776 Sales
## 650 650 40 Yes Travel_Rarely 1329 Research & Development
## 651 651 35 No Travel_Rarely 992 Research & Development
## 652 652 60 No Travel_Rarely 696 Sales
## 653 653 40 No Travel_Frequently 593 Research & Development
## 654 654 48 No Travel_Rarely 530 Sales
## 655 655 22 Yes Travel_Frequently 1368 Research & Development
## 656 656 26 No Travel_Frequently 496 Research & Development
## 657 657 51 No Travel_Rarely 1469 Research & Development
## 658 658 54 No Travel_Frequently 1050 Research & Development
## 659 659 41 No Travel_Rarely 483 Research & Development
## 660 660 36 No Travel_Frequently 1195 Research & Development
## 661 661 32 No Travel_Frequently 430 Research & Development
## 662 662 34 No Travel_Rarely 1303 Research & Development
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## 664 664 35 No Travel_Rarely 802 Research & Development
## 665 665 34 No Non-Travel 1065 Sales
## 666 666 29 No Travel_Frequently 461 Research & Development
## 667 667 29 No Travel_Frequently 1413 Sales
## 668 668 33 Yes Travel_Rarely 587 Research & Development
## 669 669 32 Yes Travel_Rarely 1089 Research & Development
## 670 670 54 No Travel_Rarely 685 Research & Development
## 671 671 37 No Travel_Rarely 397 Research & Development
## 672 672 33 No Travel_Rarely 392 Sales
## 673 673 28 No Travel_Frequently 467 Sales
## 674 674 35 Yes Travel_Frequently 130 Research & Development
## 675 675 27 No Travel_Rarely 486 Research & Development
## 676 676 46 No Travel_Rarely 168 Sales
## 677 677 36 No Travel_Rarely 676 Research & Development
## 678 678 40 No Travel_Frequently 692 Research & Development
## 679 679 36 No Travel_Frequently 688 Research & Development
## 680 680 31 No Travel_Rarely 154 Sales
## 681 681 25 No Travel_Rarely 583 Sales
## 682 682 43 No Travel_Frequently 313 Research & Development
## 683 683 36 No Travel_Rarely 1351 Research & Development
## 684 684 57 No Travel_Rarely 334 Research & Development
## 685 685 28 No Travel_Rarely 440 Research & Development
## 686 686 31 No Travel_Rarely 1274 Research & Development
## 687 687 29 Yes Travel_Frequently 337 Research & Development
## 688 688 33 No Travel_Rarely 147 Research & Development
## 689 689 19 Yes Travel_Rarely 303 Research & Development
## 690 690 38 No Non-Travel 1336 Human Resources
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## 693 693 42 No Travel_Rarely 1332 Research & Development
## 694 694 30 Yes Travel_Frequently 464 Research & Development
## 695 695 37 No Travel_Frequently 1278 Sales
## 696 696 47 No Travel_Rarely 1454 Sales
## 697 697 48 No Travel_Frequently 365 Research & Development
## 698 698 36 No Travel_Rarely 1396 Research & Development
## 699 699 18 Yes Travel_Frequently 544 Sales
## 700 700 46 No Travel_Rarely 1450 Research & Development
## 701 701 29 No Travel_Rarely 352 Human Resources
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## 704 704 26 Yes Travel_Rarely 920 Human Resources
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## 706 706 41 No Non-Travel 267 Sales
## 707 707 54 No Travel_Rarely 431 Research & Development
## 708 708 31 No Travel_Rarely 1398 Human Resources
## 709 709 38 No Travel_Rarely 1153 Research & Development
## 710 710 50 No Non-Travel 881 Research & Development
## 711 711 30 No Travel_Rarely 634 Research & Development
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## 713 713 48 No Travel_Rarely 969 Research & Development
## 714 714 36 No Travel_Frequently 541 Sales
## 715 715 36 No Non-Travel 1434 Sales
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## 718 718 28 No Travel_Rarely 857 Research & Development
## 719 719 35 Yes Travel_Frequently 880 Sales
## 720 720 28 No Travel_Rarely 950 Research & Development
## 721 721 37 No Travel_Rarely 482 Research & Development
## 722 722 37 No Travel_Rarely 671 Research & Development
## 723 723 34 No Travel_Frequently 560 Research & Development
## 724 724 36 No Travel_Rarely 796 Research & Development
## 725 725 42 No Travel_Rarely 1142 Research & Development
## 726 726 25 No Travel_Rarely 685 Research & Development
## 727 727 37 No Travel_Frequently 1231 Sales
## 728 728 34 No Travel_Frequently 878 Research & Development
## 729 729 26 Yes Travel_Rarely 471 Research & Development
## 730 730 22 No Travel_Rarely 594 Research & Development
## 731 731 43 No Travel_Rarely 177 Research & Development
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## 733 733 27 Yes Travel_Frequently 1337 Human Resources
## 734 734 32 Yes Non-Travel 1474 Sales
## 735 735 34 No Travel_Rarely 479 Research & Development
## 736 736 27 No Travel_Rarely 511 Sales
## 737 737 39 No Travel_Rarely 722 Sales
## 738 738 59 No Travel_Rarely 326 Sales
## 739 739 35 No Travel_Rarely 1219 Sales
## 740 740 35 No Travel_Rarely 538 Research & Development
## 741 741 25 No Travel_Rarely 959 Sales
## 742 742 31 No Travel_Rarely 688 Sales
## 743 743 37 No Travel_Rarely 674 Research & Development
## 744 744 23 No Travel_Rarely 160 Research & Development
## 745 745 25 No Travel_Rarely 1382 Sales
## 746 746 24 No Travel_Rarely 350 Research & Development
## 747 747 29 Yes Travel_Rarely 350 Human Resources
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## 749 749 24 No Travel_Rarely 506 Research & Development
## 750 750 33 No Travel_Rarely 134 Research & Development
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## 752 752 31 Yes Travel_Frequently 1060 Sales
## 753 753 50 No Travel_Frequently 1234 Research & Development
## 754 754 46 Yes Travel_Rarely 1254 Sales
## 755 755 54 No Travel_Rarely 1217 Research & Development
## 756 756 44 No Non-Travel 381 Research & Development
## 757 757 35 No Travel_Rarely 1137 Research & Development
## 758 758 49 No Travel_Rarely 1495 Research & Development
## 759 759 38 No Travel_Rarely 1495 Research & Development
## 760 760 45 Yes Travel_Rarely 1449 Sales
## 761 761 29 Yes Travel_Rarely 906 Research & Development
## 762 762 47 No Travel_Rarely 202 Research & Development
## 763 763 41 No Non-Travel 256 Sales
## 764 764 50 No Non-Travel 145 Sales
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## 766 766 45 No Travel_Rarely 1015 Research & Development
## 767 767 41 No Travel_Rarely 509 Research & Development
## 768 768 40 No Non-Travel 1094 Sales
## 769 769 46 No Travel_Rarely 150 Research & Development
## 770 770 45 No Non-Travel 1050 Sales
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## 772 772 46 No Travel_Rarely 526 Sales
## 773 773 39 No Travel_Frequently 505 Research & Development
## 774 774 36 Yes Travel_Rarely 318 Research & Development
## 775 775 42 No Travel_Frequently 288 Research & Development
## 776 776 23 No Travel_Rarely 310 Research & Development
## 777 777 30 No Travel_Rarely 330 Human Resources
## 778 778 32 No Non-Travel 862 Sales
## 779 779 26 No Non-Travel 786 Research & Development
## 780 780 29 Yes Travel_Rarely 1092 Research & Development
## 781 781 41 Yes Travel_Rarely 1356 Sales
## 782 782 39 No Travel_Rarely 408 Research & Development
## 783 783 33 No Travel_Rarely 1075 Human Resources
## 784 784 33 No Travel_Frequently 1146 Sales
## 785 785 47 No Non-Travel 543 Sales
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## 787 787 51 No Travel_Rarely 942 Research & Development
## 788 788 52 Yes Travel_Rarely 723 Research & Development
## 789 789 55 No Travel_Rarely 478 Research & Development
## 790 790 25 No Travel_Rarely 266 Research & Development
## 791 791 49 No Travel_Rarely 271 Research & Development
## 792 792 34 No Travel_Rarely 629 Research & Development
## 793 793 38 No Travel_Rarely 397 Research & Development
## 794 794 36 No Travel_Rarely 1223 Research & Development
## 795 795 49 No Non-Travel 1002 Research & Development
## 796 796 33 Yes Travel_Frequently 1076 Research & Development
## 797 797 45 No Travel_Rarely 1385 Research & Development
## 798 798 22 Yes Travel_Rarely 1294 Research & Development
## 799 799 19 Yes Non-Travel 504 Research & Development
## 800 800 36 No Travel_Rarely 1040 Research & Development
## 801 801 33 No Travel_Frequently 1141 Sales
## 802 802 31 No Travel_Rarely 828 Sales
## 803 803 39 No Travel_Rarely 117 Research & Development
## 804 804 35 Yes Travel_Rarely 1204 Sales
## 805 805 44 No Non-Travel 489 Research & Development
## 806 806 36 No Travel_Rarely 1383 Research & Development
## 807 807 25 No Travel_Rarely 1372 Sales
## 808 808 22 No Travel_Rarely 1256 Research & Development
## 809 809 30 No Travel_Rarely 793 Research & Development
## 810 810 27 No Travel_Rarely 1157 Research & Development
## 811 811 30 No Non-Travel 111 Research & Development
## 812 812 25 No Travel_Rarely 949 Research & Development
## 813 813 24 No Travel_Rarely 1371 Sales
## 814 814 47 No Travel_Rarely 1176 Human Resources
## 815 815 38 No Travel_Rarely 395 Sales
## 816 816 33 No Travel_Rarely 117 Research & Development
## 817 817 30 No Travel_Rarely 946 Research & Development
## 818 818 33 No Travel_Rarely 213 Research & Development
## 819 819 30 No Travel_Rarely 1427 Research & Development
## 820 820 28 Yes Travel_Rarely 1475 Sales
## 821 821 32 No Travel_Rarely 634 Research & Development
## 822 822 29 No Travel_Rarely 1247 Sales
## 823 823 34 No Travel_Rarely 704 Sales
## 824 824 31 Yes Travel_Frequently 874 Research & Development
## 825 825 36 No Travel_Rarely 559 Research & Development
## 826 826 46 No Travel_Rarely 430 Research & Development
## 827 827 31 Yes Travel_Rarely 1079 Sales
## 828 828 34 No Travel_Rarely 131 Sales
## 829 829 38 No Travel_Frequently 594 Research & Development
## 830 830 50 No Travel_Frequently 1246 Human Resources
## 831 831 45 No Non-Travel 248 Research & Development
## 832 832 19 Yes Travel_Rarely 528 Sales
## 833 833 28 No Travel_Rarely 1179 Research & Development
## 834 834 19 No Travel_Rarely 645 Research & Development
## 835 835 55 No Non-Travel 177 Research & Development
## 836 836 37 No Non-Travel 142 Sales
## 837 837 37 No Travel_Rarely 1439 Research & Development
## 838 838 34 No Travel_Rarely 258 Sales
## 839 839 47 No Travel_Rarely 1001 Research & Development
## 840 840 48 No Travel_Rarely 1224 Research & Development
## 841 841 18 Yes Travel_Rarely 230 Research & Development
## 842 842 29 No Travel_Rarely 332 Human Resources
## 843 843 52 No Travel_Frequently 890 Research & Development
## 844 844 34 No Travel_Rarely 943 Research & Development
## 845 845 34 No Travel_Rarely 511 Sales
## 846 846 28 No Travel_Rarely 1300 Research & Development
## 847 847 19 No Travel_Rarely 1181 Research & Development
## 848 848 39 No Travel_Rarely 1132 Research & Development
## 849 849 40 Yes Travel_Rarely 575 Sales
## 850 850 26 No Travel_Frequently 1096 Research & Development
## 851 851 31 Yes Travel_Rarely 542 Sales
## 852 852 30 No Travel_Rarely 125 Research & Development
## 853 853 29 No Travel_Rarely 153 Research & Development
## 854 854 28 No Travel_Rarely 1476 Research & Development
## 855 855 35 No Travel_Rarely 219 Research & Development
## 856 856 35 No Travel_Rarely 660 Sales
## 857 857 30 Yes Travel_Rarely 1005 Research & Development
## 858 858 31 No Travel_Frequently 798 Research & Development
## 859 859 41 No Travel_Rarely 933 Research & Development
## 860 860 44 Yes Travel_Rarely 621 Research & Development
## 861 861 51 No Travel_Frequently 968 Research & Development
## 862 862 43 No Travel_Rarely 531 Sales
## 863 863 34 Yes Non-Travel 1362 Sales
## 864 864 47 No Travel_Frequently 217 Sales
## 865 865 45 No Travel_Rarely 1448 Research & Development
## 866 866 48 No Travel_Rarely 855 Research & Development
## 867 867 32 No Non-Travel 976 Sales
## 868 868 47 No Travel_Rarely 571 Sales
## 869 869 45 No Travel_Rarely 1457 Research & Development
## 870 870 35 No Travel_Frequently 138 Research & Development
## 871 1171 35 <NA> Travel_Rarely 750 Research & Development
## 872 1172 33 <NA> Travel_Rarely 147 Human Resources
## 873 1173 26 <NA> Travel_Rarely 1330 Research & Development
## 874 1174 55 <NA> Travel_Rarely 1311 Research & Development
## 875 1175 29 <NA> Travel_Rarely 1246 Sales
## 876 1176 51 <NA> Travel_Frequently 1456 Research & Development
## 877 1177 52 <NA> Non-Travel 585 Sales
## 878 1178 39 <NA> Travel_Rarely 1387 Research & Development
## 879 1179 31 <NA> Travel_Rarely 1062 Research & Development
## 880 1180 31 <NA> Travel_Frequently 534 Research & Development
## 881 1181 40 <NA> Travel_Frequently 1469 Research & Development
## 882 1182 31 <NA> Travel_Rarely 1082 Research & Development
## 883 1183 33 <NA> Non-Travel 775 Research & Development
## 884 1184 50 <NA> Travel_Rarely 691 Research & Development
## 885 1185 33 <NA> Travel_Rarely 516 Research & Development
## 886 1186 42 <NA> Non-Travel 335 Research & Development
## 887 1187 33 <NA> Travel_Rarely 575 Research & Development
## 888 1188 29 <NA> Travel_Rarely 694 Research & Development
## 889 1189 55 <NA> Travel_Rarely 436 Sales
## 890 1190 49 <NA> Travel_Rarely 1184 Sales
## 891 1191 32 <NA> Travel_Rarely 267 Research & Development
## 892 1192 50 <NA> Travel_Rarely 1126 Research & Development
## 893 1193 54 <NA> Travel_Rarely 157 Research & Development
## 894 1194 24 <NA> Travel_Frequently 897 Human Resources
## 895 1195 45 <NA> Travel_Rarely 1038 Research & Development
## 896 1196 36 <NA> Non-Travel 635 Sales
## 897 1197 41 <NA> Non-Travel 552 Human Resources
## 898 1198 21 <NA> Travel_Rarely 1343 Sales
## 899 1199 36 <NA> Travel_Rarely 335 Sales
## 900 1200 30 <NA> Travel_Frequently 109 Research & Development
## 901 1201 35 <NA> Travel_Frequently 146 Research & Development
## 902 1202 46 <NA> Travel_Rarely 717 Research & Development
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## 875 2 2 Sales Executive 3
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## 918 2 2 Sales Executive 2
## 919 4 1 Laboratory Technician 3
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## 975 2 1 Sales Representative 1
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## 1001 3 1 Laboratory Technician 3
## 1002 3 4 Research Director 1
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## 1008 2 1 Laboratory Technician 2
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## 1038 3 2 Sales Executive 2
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## 1049 3 5 Manager 4
## 1050 4 4 Sales Executive 4
## 1051 1 1 Laboratory Technician 4
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## 1054 2 2 Sales Executive 2
## 1055 2 4 Research Director 2
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## 1058 2 1 Laboratory Technician 3
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## 1060 3 2 Manufacturing Director 3
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## 1074 1 3 Manufacturing Director 1
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## 1077 3 2 Sales Executive 4
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## 1081 3 1 Research Scientist 1
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## 1108 1 2 Healthcare Representative 1
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## 1110 3 4 Manager 2
## 1111 3 1 Research Scientist 3
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## 1114 3 2 Sales Executive 2
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## 1169 4 5 Manager 4
## 1170 3 2 Sales Executive 4
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## 1 Divorced 4403 9250 2 Y
## 2 Single 19626 17544 1 Y
## 3 Single 9362 19944 2 Y
## 4 Married 10422 24032 1 Y
## 5 Single 3760 17218 1 Y
## 6 Divorced 8793 4809 1 Y
## 7 Married 2127 5561 2 Y
## 8 Divorced 6694 24223 2 Y
## 9 Married 2220 18410 1 Y
## 10 Married 5063 15332 1 Y
## 11 Married 19392 22539 7 Y
## 12 Divorced 2706 10494 1 Y
## 13 Single 10221 18869 3 Y
## 14 Married 2476 17434 1 Y
## 15 Single 3423 22957 6 Y
## 16 Married 6932 24406 1 Y
## 17 Married 6725 13554 1 Y
## 18 Married 5258 16044 2 Y
## 19 Married 2932 5586 0 Y
## 20 Married 5033 9364 2 Y
## 21 Divorced 8847 13934 2 Y
## 22 Married 8120 18597 3 Y
## 23 Divorced 5679 19627 3 Y
## 24 Single 5332 21602 7 Y
## 25 Single 6949 12291 0 Y
## 26 Married 18213 8751 7 Y
## 27 Married 9924 12355 0 Y
## 28 Single 8224 18385 0 Y
## 29 Single 10448 5843 6 Y
## 30 Married 4258 26589 0 Y
## 31 Divorced 4668 22812 0 Y
## 32 Married 2028 13637 1 Y
## 33 Married 16872 14977 3 Y
## 34 Married 2258 16340 6 Y
## 35 Single 9679 10138 8 Y
## 36 Married 2794 26062 1 Y
## 37 Married 10306 21530 9 Y
## 38 Divorced 6134 8658 5 Y
## 39 Single 19717 4022 6 Y
## 40 Married 2791 21981 0 Y
## 41 Divorced 16291 22577 4 Y
## 42 Single 6632 12388 0 Y
## 43 Divorced 2587 24941 4 Y
## 44 Single 19613 26362 8 Y
## 45 Married 1223 16901 1 Y
## 46 Single 3673 16458 1 Y
## 47 Single 6735 12147 6 Y
## 48 Divorced 8740 5569 0 Y
## 49 Single 1878 8059 1 Y
## 50 Divorced 2024 5970 6 Y
## 51 Single 2619 14561 3 Y
## 52 Married 19701 22456 3 Y
## 53 Married 2011 19982 1 Y
## 54 Married 4447 23163 1 Y
## 55 Single 4424 20682 1 Y
## 56 Divorced 2289 20520 1 Y
## 57 Divorced 2759 20366 6 Y
## 58 Single 4723 16213 1 Y
## 59 Divorced 13603 11677 2 Y
## 60 Single 1118 8040 1 Y
## 61 Married 10325 5518 1 Y
## 62 Married 7094 5747 3 Y
## 63 Married 3067 6393 0 Y
## 64 Single 1514 8018 1 Y
## 65 Married 2210 3372 1 Y
## 66 Divorced 3298 15053 0 Y
## 67 Single 6646 19368 1 Y
## 68 Single 2647 13672 1 Y
## 69 Married 2194 5868 4 Y
## 70 Single 2115 15881 1 Y
## 71 Married 2782 21412 3 Y
## 72 Single 8463 23490 0 Y
## 73 Married 5916 15497 3 Y
## 74 Married 4766 9051 3 Y
## 75 Single 8446 21534 9 Y
## 76 Single 4011 10781 1 Y
## 77 Married 7083 12288 1 Y
## 78 Single 17567 3156 1 Y
## 79 Single 8865 16840 6 Y
## 80 Divorced 1129 17536 1 Y
## 81 Married 4777 14382 5 Y
## 82 Married 6883 5151 2 Y
## 83 Married 5321 14284 2 Y
## 84 Divorced 5974 17001 4 Y
## 85 Single 5410 2323 9 Y
## 86 Single 13726 21829 3 Y
## 87 Single 4960 11825 2 Y
## 88 Divorced 3743 10074 1 Y
## 89 Single 8726 2975 1 Y
## 90 Single 18947 22822 3 Y
## 91 Married 1702 12106 1 Y
## 92 Married 7336 11162 1 Y
## 93 Single 2090 2396 6 Y
## 94 Single 6142 4223 3 Y
## 95 Single 19926 17053 3 Y
## 96 Married 4907 24532 1 Y
## 97 Married 16606 11380 8 Y
## 98 Divorced 5163 15850 5 Y
## 99 Married 3180 4668 0 Y
## 100 Married 3294 3708 5 Y
## 101 Married 2099 7679 0 Y
## 102 Divorced 10793 8386 1 Y
## 103 Married 6538 12740 9 Y
## 104 Married 2819 8544 2 Y
## 105 Single 16598 19764 4 Y
## 106 Married 4420 13421 1 Y
## 107 Married 4319 26283 1 Y
## 108 Married 19636 25811 4 Y
## 109 Married 8926 10842 4 Y
## 110 Single 2439 11288 1 Y
## 111 Divorced 2703 22088 1 Y
## 112 Married 17099 13829 2 Y
## 113 Single 10445 15322 7 Y
## 114 Divorced 5093 4761 2 Y
## 115 Married 8966 21026 3 Y
## 116 Single 7642 4814 1 Y
## 117 Single 4950 20623 0 Y
## 118 Married 4025 23679 4 Y
## 119 Divorced 2451 4609 4 Y
## 120 Single 6687 6163 1 Y
## 121 Married 11713 20335 9 Y
## 122 Single 2380 25479 1 Y
## 123 Divorced 10761 19239 4 Y
## 124 Single 4028 7791 0 Y
## 125 Single 4554 20260 1 Y
## 126 Married 5204 13586 1 Y
## 127 Single 3919 4681 1 Y
## 128 Married 5906 23888 0 Y
## 129 Single 4081 20003 1 Y
## 130 Divorced 13225 7739 2 Y
## 131 Divorced 4187 3356 1 Y
## 132 Single 4507 8191 3 Y
## 133 Married 4523 4386 0 Y
## 134 Married 5295 7693 4 Y
## 135 Divorced 5257 6227 1 Y
## 136 Married 4244 9931 1 Y
## 137 Married 2319 6689 1 Y
## 138 Married 10739 13943 8 Y
## 139 Married 2404 4303 7 Y
## 140 Single 7428 14506 2 Y
## 141 Married 13402 18235 4 Y
## 142 Divorced 2372 26076 1 Y
## 143 Divorced 10845 24208 6 Y
## 144 Married 6502 22825 4 Y
## 145 Divorced 6377 13888 5 Y
## 146 Married 4649 16928 1 Y
## 147 Married 2756 4673 1 Y
## 148 Married 6392 10589 2 Y
## 149 Single 3597 6409 8 Y
## 150 Divorced 4877 20460 0 Y
## 151 Divorced 4233 11512 2 Y
## 152 Single 5396 21703 1 Y
## 153 Married 5484 16985 1 Y
## 154 Single 6870 15530 3 Y
## 155 Divorced 2008 6896 1 Y
## 156 Married 4736 6069 7 Y
## 157 Married 5677 4258 3 Y
## 158 Divorced 12169 13547 7 Y
## 159 Married 10820 11535 8 Y
## 160 Married 13191 23281 3 Y
## 161 Married 5126 15998 1 Y
## 162 Single 8722 12355 1 Y
## 163 Divorced 7104 20431 0 Y
## 164 Single 6545 23016 3 Y
## 165 Married 8020 5100 0 Y
## 166 Single 2044 22052 1 Y
## 167 Married 16413 3498 3 Y
## 168 Divorced 16184 22578 4 Y
## 169 Divorced 5577 22087 3 Y
## 170 Divorced 2514 26968 4 Y
## 171 Divorced 5304 25275 7 Y
## 172 Married 5467 2125 8 Y
## 173 Married 10527 8984 5 Y
## 174 Divorced 8380 21708 0 Y
## 175 Divorced 14852 13938 6 Y
## 176 Single 2070 25326 1 Y
## 177 Married 19859 21199 5 Y
## 178 Married 2042 15346 6 Y
## 179 Single 8998 15589 1 Y
## 180 Single 8686 12930 4 Y
## 181 Divorced 10274 19588 2 Y
## 182 Divorced 6893 19461 3 Y
## 183 Single 4739 16090 4 Y
## 184 Single 8376 9150 4 Y
## 185 Married 3755 17872 1 Y
## 186 Married 2070 2613 1 Y
## 187 Divorced 5206 4973 1 Y
## 188 Divorced 15972 21086 6 Y
## 189 Divorced 19144 15815 3 Y
## 190 Married 4221 8863 1 Y
## 191 Married 5775 11934 1 Y
## 192 Married 9637 8277 2 Y
## 193 Divorced 4810 26314 2 Y
## 194 Married 4051 19658 2 Y
## 195 Single 3904 4050 0 Y
## 196 Married 2723 23231 1 Y
## 197 Married 2075 18725 3 Y
## 198 Married 13120 11879 6 Y
## 199 Married 10248 2094 3 Y
## 200 Married 2127 9100 1 Y
## 201 Single 13194 17071 4 Y
## 202 Married 6220 7346 1 Y
## 203 Married 6162 19124 1 Y
## 204 Single 2216 3872 7 Y
## 205 Married 3931 20990 2 Y
## 206 Married 2559 7508 1 Y
## 207 Single 5346 9489 8 Y
## 208 Married 6673 11354 7 Y
## 209 Divorced 3204 10415 5 Y
## 210 Divorced 8189 21196 3 Y
## 211 Married 13570 5640 0 Y
## 212 Married 18303 7770 6 Y
## 213 Single 4680 15232 3 Y
## 214 Married 6062 4051 9 Y
## 215 Single 2045 15174 0 Y
## 216 Divorced 2345 8045 2 Y
## 217 Married 9824 22174 3 Y
## 218 Divorced 17174 2437 3 Y
## 219 Single 2632 23910 1 Y
## 220 Single 2875 9973 1 Y
## 221 Single 6274 18686 1 Y
## 222 Married 4286 5630 2 Y
## 223 Divorced 5368 16130 1 Y
## 224 Divorced 1563 12530 1 Y
## 225 Divorced 4771 14293 2 Y
## 226 Married 3748 4077 1 Y
## 227 Divorced 1951 10910 1 Y
## 228 Divorced 2328 12392 1 Y
## 229 Married 4999 17519 0 Y
## 230 Married 13966 11652 2 Y
## 231 Divorced 5770 5388 1 Y
## 232 Married 2014 9687 1 Y
## 233 Married 12936 24164 7 Y
## 234 Married 5940 17011 2 Y
## 235 Married 16959 19494 1 Y
## 236 Married 10609 14922 5 Y
## 237 Married 18880 17312 5 Y
## 238 Married 6322 18089 1 Y
## 239 Married 2187 19655 4 Y
## 240 Divorced 13269 21981 5 Y
## 241 Married 7406 6950 1 Y
## 242 Single 18061 13035 3 Y
## 243 Married 2703 4956 0 Y
## 244 Married 4400 15182 3 Y
## 245 Married 5056 17689 1 Y
## 246 Divorced 2532 6054 6 Y
## 247 Single 7143 25713 1 Y
## 248 Married 2269 18024 0 Y
## 249 Divorced 5467 13953 3 Y
## 250 Married 16595 5626 7 Y
## 251 Divorced 2008 20439 1 Y
## 252 Divorced 5079 25952 4 Y
## 253 Single 2274 6153 1 Y
## 254 Married 2956 21495 0 Y
## 255 Divorced 13757 25178 2 Y
## 256 Single 2438 24978 4 Y
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## 921 Married 16856 10084 1 Y
## 922 Married 2909 23159 1 Y
## 923 Divorced 2314 9148 0 Y
## 924 Married 4936 14862 4 Y
## 925 Married 5902 14590 4 Y
## 926 Divorced 19197 8213 1 Y
## 927 Single 2686 5207 1 Y
## 928 Married 2097 16734 4 Y
## 929 Divorced 7140 3088 2 Y
## 930 Married 2818 5044 2 Y
## 931 Married 18041 13022 0 Y
## 932 Married 3477 18103 1 Y
## 933 Single 2600 18275 1 Y
## 934 Single 13758 2447 0 Y
## 935 Single 11244 21072 2 Y
## 936 Single 5324 26507 5 Y
## 937 Single 14275 20206 6 Y
## 938 Married 9998 19293 6 Y
## 939 Married 2400 5530 0 Y
## 940 Married 2372 5628 6 Y
## 941 Single 6540 19394 9 Y
## 942 Single 4541 7744 1 Y
## 943 Divorced 5485 22670 9 Y
## 944 Married 4284 13588 5 Y
## 945 Married 5562 21782 4 Y
## 946 Single 2080 4732 2 Y
## 947 Married 6755 2967 2 Y
## 948 Single 4968 26427 0 Y
## 949 Married 3692 9256 1 Y
## 950 Married 2143 25527 4 Y
## 951 Married 5429 17491 4 Y
## 952 Divorced 9725 12278 0 Y
## 953 Divorced 3072 19877 2 Y
## 954 Married 4490 21833 4 Y
## 955 Married 5415 15972 3 Y
## 956 Married 13247 9731 2 Y
## 957 Single 11994 21293 0 Y
## 958 Single 19068 21030 1 Y
## 959 Single 10965 12066 8 Y
## 960 Single 5326 3064 6 Y
## 961 Married 7879 14810 1 Y
## 962 Married 6472 8989 1 Y
## 963 Single 3445 6152 1 Y
## 964 Married 18172 9755 3 Y
## 965 Married 4639 11262 1 Y
## 966 Divorced 4127 19188 2 Y
## 967 Divorced 10883 20467 3 Y
## 968 Married 5151 12315 1 Y
## 969 Single 2058 19757 0 Y
## 970 Single 1261 22262 1 Y
## 971 Single 13591 14674 3 Y
## 972 Single 8474 20925 1 Y
## 973 Married 3691 4605 1 Y
## 974 Married 17584 21016 3 Y
## 975 Married 2033 14470 1 Y
## 976 Married 5472 3334 1 Y
## 977 Married 5562 19711 3 Y
## 978 Divorced 6583 20115 2 Y
## 979 Divorced 2073 23648 4 Y
## 980 Single 17328 13871 2 Y
## 981 Married 4614 23288 0 Y
## 982 Single 8095 18264 0 Y
## 983 Divorced 5731 17171 7 Y
## 984 Married 6261 4185 3 Y
## 985 Married 17875 11761 4 Y
## 986 Married 6804 23683 3 Y
## 987 Married 3579 9369 0 Y
## 988 Married 7639 24525 1 Y
## 989 Divorced 2694 26551 1 Y
## 990 Single 13872 24409 0 Y
## 991 Married 4440 7636 0 Y
## 992 Married 7969 19609 2 Y
## 993 Married 19331 19519 4 Y
## 994 Married 2571 12290 4 Y
## 995 Married 4639 2261 2 Y
## 996 Married 11836 22789 5 Y
## 997 Single 2642 2755 1 Y
## 998 Married 17046 9314 0 Y
## 999 Married 19406 8509 4 Y
## 1000 Married 4374 15411 0 Y
## 1001 Married 2339 3666 8 Y
## 1002 Married 16328 22074 3 Y
## 1003 Single 13964 17810 7 Y
## 1004 Single 9907 26186 7 Y
## 1005 Married 10239 18092 3 Y
## 1006 Divorced 11996 19100 7 Y
## 1007 Married 6586 4821 0 Y
## 1008 Married 4841 24052 4 Y
## 1009 Married 4621 5869 1 Y
## 1010 Single 7991 25166 8 Y
## 1011 Divorced 2348 10901 8 Y
## 1012 Divorced 18430 16225 1 Y
## 1013 Single 7945 19948 6 Y
## 1014 Single 3038 12430 3 Y
## 1015 Divorced 7441 20933 1 Y
## 1016 Single 9355 9558 1 Y
## 1017 Single 4272 9558 4 Y
## 1018 Single 1359 16154 1 Y
## 1019 Single 3310 4488 1 Y
## 1020 Married 19586 23037 1 Y
## 1021 Married 2911 14776 1 Y
## 1022 Single 2450 10919 2 Y
## 1023 Married 13503 14115 1 Y
## 1024 Single 9667 2739 9 Y
## 1025 Single 2342 22929 0 Y
## 1026 Married 2218 16193 1 Y
## 1027 Divorced 2029 15891 1 Y
## 1028 Single 9094 17235 2 Y
## 1029 Single 1232 19281 1 Y
## 1030 Married 9884 8302 2 Y
## 1031 Married 6516 5041 2 Y
## 1032 Married 6549 3173 1 Y
## 1033 Single 2960 17102 2 Y
## 1034 Divorced 3339 17285 3 Y
## 1035 Married 5231 23726 2 Y
## 1036 Single 2201 18168 9 Y
## 1037 Divorced 19847 19196 4 Y
## 1038 Married 6349 22107 0 Y
## 1039 Married 3221 3297 1 Y
## 1040 Single 4859 6698 1 Y
## 1041 Single 3424 21632 7 Y
## 1042 Married 2362 14669 4 Y
## 1043 Single 6811 23398 8 Y
## 1044 Single 2288 6319 1 Y
## 1045 Divorced 3816 17881 1 Y
## 1046 Married 5130 24907 1 Y
## 1047 Married 3580 10554 2 Y
## 1048 Married 2690 7713 1 Y
## 1049 Married 19513 9358 4 Y
## 1050 Married 13320 11737 3 Y
## 1051 Divorced 4364 5288 3 Y
## 1052 Divorced 2645 21923 1 Y
## 1053 Married 3690 3425 2 Y
## 1054 Divorced 8938 12227 2 Y
## 1055 Divorced 14336 4345 1 Y
## 1056 Single 8381 7507 7 Y
## 1057 Single 16704 17119 1 Y
## 1058 Single 3033 12828 1 Y
## 1059 Divorced 10502 9659 7 Y
## 1060 Single 4534 13352 0 Y
## 1061 Divorced 5209 19760 1 Y
## 1062 Divorced 3069 10302 0 Y
## 1063 Married 5208 26312 1 Y
## 1064 Married 2821 2997 3 Y
## 1065 Married 2096 26376 1 Y
## 1066 Single 3983 7621 0 Y
## 1067 Divorced 9950 11533 9 Y
## 1068 Married 4345 4381 0 Y
## 1069 Divorced 14026 17588 1 Y
## 1070 Married 2897 22474 3 Y
## 1071 Divorced 19419 3735 2 Y
## 1072 Married 2044 5531 1 Y
## 1073 Divorced 2232 15417 7 Y
## 1074 Divorced 10252 4235 2 Y
## 1075 Married 12965 22308 4 Y
## 1076 Married 3929 6984 8 Y
## 1077 Single 5253 20750 1 Y
## 1078 Single 5147 10697 8 Y
## 1079 Single 4198 16379 2 Y
## 1080 Divorced 19517 24118 3 Y
## 1081 Single 3102 6582 0 Y
## 1082 Married 3697 9278 9 Y
## 1083 Single 5980 26085 6 Y
## 1084 Married 5175 22162 5 Y
## 1085 Divorced 2576 20490 3 Y
## 1086 Married 2308 4944 0 Y
## 1087 Married 2451 22376 6 Y
## 1088 Married 6179 21057 1 Y
## 1089 Divorced 2904 16092 1 Y
## 1090 Single 7779 23238 2 Y
## 1091 Divorced 6118 5431 1 Y
## 1092 Divorced 2297 17967 1 Y
## 1093 Married 19232 4933 1 Y
## 1094 Single 19537 6462 7 Y
## 1095 Married 4775 19146 6 Y
## 1096 Married 7632 14295 4 Y
## 1097 Single 4249 2690 1 Y
## 1098 Married 5689 24594 1 Y
## 1099 Married 7457 13273 2 Y
## 1100 Divorced 3034 26914 1 Y
## 1101 Divorced 2074 26619 1 Y
## 1102 Married 4194 14363 1 Y
## 1103 Divorced 19190 17477 1 Y
## 1104 Single 4230 19225 0 Y
## 1105 Married 5617 21075 1 Y
## 1106 Single 2506 13301 3 Y
## 1107 Single 2496 6670 4 Y
## 1108 Married 9715 7288 3 Y
## 1109 Single 19045 18938 0 Y
## 1110 Single 16835 9873 3 Y
## 1111 Single 3375 17624 0 Y
## 1112 Single 2718 17674 2 Y
## 1113 Married 19237 12853 2 Y
## 1114 Single 9278 20763 3 Y
## 1115 Divorced 5605 8504 1 Y
## 1116 Married 5488 20161 1 Y
## 1117 Married 3722 21081 6 Y
## 1118 Married 2659 17759 1 Y
## 1119 Single 7446 8931 1 Y
## 1120 Married 3506 6020 0 Y
## 1121 Single 2313 2993 4 Y
## 1122 Divorced 19973 20284 1 Y
## 1123 Divorced 19141 8861 3 Y
## 1124 Married 1706 16571 1 Y
## 1125 Married 17465 15596 3 Y
## 1126 Single 10496 2755 6 Y
## 1127 Married 6434 5118 4 Y
## 1128 Single 4695 12858 7 Y
## 1129 Single 5828 8450 1 Y
## 1130 Divorced 16823 18991 2 Y
## 1131 Divorced 14732 12414 2 Y
## 1132 Divorced 9852 8935 1 Y
## 1133 Single 17444 20489 1 Y
## 1134 Divorced 6728 14255 7 Y
## 1135 Divorced 4011 8232 0 Y
## 1136 Married 2132 4585 4 Y
## 1137 Married 7005 3458 3 Y
## 1138 Divorced 5460 6219 4 Y
## 1139 Single 3646 17181 2 Y
## 1140 Divorced 10388 6975 1 Y
## 1141 Married 5405 4244 2 Y
## 1142 Married 1601 3445 1 Y
## 1143 Single 9936 3787 0 Y
## 1144 Single 2716 25422 1 Y
## 1145 Single 2564 18437 1 Y
## 1146 Single 2062 19384 3 Y
## 1147 Single 4294 11148 1 Y
## 1148 Married 7510 16873 1 Y
## 1149 Married 9610 3840 3 Y
## 1150 Single 2871 23785 1 Y
## 1151 Divorced 2367 18779 5 Y
## 1152 Divorced 2720 18959 1 Y
## 1153 Married 10686 8392 6 Y
## 1154 Married 2942 12154 2 Y
## 1155 Single 5765 17485 5 Y
## 1156 Single 3989 20586 1 Y
## 1157 Divorced 2778 17725 4 Y
## 1158 Single 2478 20938 1 Y
## 1159 Divorced 3161 19920 3 Y
## 1160 Married 2773 12145 0 Y
## 1161 Single 5933 5197 9 Y
## 1162 Divorced 2782 19905 0 Y
## 1163 Married 2141 5348 1 Y
## 1164 Married 5390 13243 2 Y
## 1165 Married 2061 11133 1 Y
## 1166 Single 2519 12287 4 Y
## 1167 Single 5010 24301 1 Y
## 1168 Single 2293 10558 1 Y
## 1169 Married 19202 15970 0 Y
## 1170 Married 4537 17783 0 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 No 11 3 3
## 2 No 14 3 1
## 3 No 11 3 3
## 4 No 19 3 3
## 5 Yes 13 3 3
## 6 No 21 4 3
## 7 Yes 12 3 1
## 8 Yes 14 3 3
## 9 Yes 19 3 4
## 10 No 14 3 2
## 11 No 13 3 4
## 12 No 15 3 2
## 13 No 21 4 2
## 14 No 18 3 1
## 15 No 12 3 3
## 16 No 13 3 4
## 17 No 12 3 3
## 18 No 14 3 3
## 19 Yes 14 3 1
## 20 No 15 3 4
## 21 Yes 11 3 3
## 22 No 12 3 4
## 23 Yes 13 3 2
## 24 No 13 3 4
## 25 No 14 3 1
## 26 No 11 3 3
## 27 No 11 3 4
## 28 Yes 17 3 1
## 29 Yes 13 3 2
## 30 No 18 3 1
## 31 No 17 3 4
## 32 No 18 3 4
## 33 Yes 12 3 2
## 34 No 12 3 2
## 35 No 24 4 2
## 36 No 20 4 3
## 37 No 17 3 3
## 38 Yes 13 3 2
## 39 No 14 3 1
## 40 No 12 3 1
## 41 No 22 4 4
## 42 No 13 3 1
## 43 Yes 16 3 2
## 44 No 22 4 4
## 45 No 22 4 4
## 46 No 13 3 3
## 47 No 15 3 2
## 48 Yes 14 3 2
## 49 Yes 14 3 4
## 50 No 18 3 4
## 51 No 17 3 4
## 52 Yes 21 4 3
## 53 No 13 3 4
## 54 No 12 3 2
## 55 No 23 4 4
## 56 No 20 4 2
## 57 Yes 12 3 4
## 58 Yes 18 3 4
## 59 Yes 18 3 1
## 60 Yes 14 3 4
## 61 Yes 11 3 1
## 62 No 12 3 1
## 63 No 19 3 3
## 64 No 16 3 3
## 65 No 13 3 1
## 66 Yes 12 3 4
## 67 No 13 3 2
## 68 No 13 3 3
## 69 No 13 3 4
## 70 No 12 3 2
## 71 No 22 4 1
## 72 No 18 3 4
## 73 Yes 13 3 1
## 74 Yes 11 3 1
## 75 Yes 19 3 3
## 76 No 23 4 4
## 77 Yes 14 3 4
## 78 No 15 3 2
## 79 No 12 3 4
## 80 Yes 11 3 3
## 81 No 15 3 1
## 82 No 16 3 2
## 83 No 11 3 4
## 84 Yes 13 3 1
## 85 Yes 11 3 4
## 86 Yes 13 3 1
## 87 No 12 3 4
## 88 Yes 24 4 4
## 89 No 15 3 4
## 90 No 12 3 4
## 91 Yes 23 4 3
## 92 No 13 3 1
## 93 Yes 15 3 2
## 94 Yes 16 3 3
## 95 No 15 3 2
## 96 No 25 4 3
## 97 No 12 3 4
## 98 No 14 3 4
## 99 No 13 3 3
## 100 No 17 3 1
## 101 No 14 3 2
## 102 No 18 3 1
## 103 No 15 3 1
## 104 No 16 3 1
## 105 No 12 3 2
## 106 No 22 4 2
## 107 No 13 3 1
## 108 Yes 18 3 1
## 109 No 22 4 4
## 110 No 14 3 4
## 111 Yes 14 3 4
## 112 No 15 3 2
## 113 No 19 3 4
## 114 No 11 3 1
## 115 Yes 15 3 4
## 116 Yes 13 3 4
## 117 No 14 3 2
## 118 Yes 13 3 1
## 119 No 12 3 1
## 120 No 11 3 4
## 121 No 14 3 1
## 122 Yes 11 3 4
## 123 Yes 12 3 3
## 124 No 20 4 1
## 125 No 18 3 1
## 126 Yes 11 3 4
## 127 Yes 22 4 2
## 128 No 13 3 4
## 129 Yes 14 3 1
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## 131 Yes 13 3 2
## 132 No 12 3 3
## 133 No 11 3 4
## 134 No 21 4 3
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## 137 Yes 11 3 4
## 138 No 11 3 3
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## 140 No 12 3 1
## 141 Yes 12 3 1
## 142 No 12 3 4
## 143 No 13 3 2
## 144 No 14 3 2
## 145 No 20 4 2
## 146 No 14 3 1
## 147 No 13 3 4
## 148 No 13 3 4
## 149 No 22 4 4
## 150 No 21 4 2
## 151 No 17 3 3
## 152 No 12 3 4
## 153 No 14 3 3
## 154 No 12 3 1
## 155 No 14 3 2
## 156 Yes 12 3 2
## 157 No 14 3 3
## 158 No 11 3 4
## 159 No 11 3 3
## 160 Yes 17 3 3
## 161 Yes 12 3 3
## 162 No 12 3 1
## 163 No 12 3 4
## 164 Yes 13 3 3
## 165 No 15 3 3
## 166 No 13 3 4
## 167 No 16 3 2
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## 170 No 22 4 1
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## 172 No 18 3 3
## 173 No 11 3 4
## 174 Yes 14 3 4
## 175 No 13 3 3
## 176 Yes 11 3 3
## 177 Yes 13 3 4
## 178 Yes 14 3 2
## 179 No 14 3 4
## 180 No 22 4 3
## 181 No 18 3 2
## 182 No 15 3 4
## 183 No 12 3 4
## 184 No 18 3 4
## 185 No 11 3 1
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## 187 No 17 3 3
## 188 No 14 3 3
## 189 No 14 3 1
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## 191 No 13 3 4
## 192 No 14 3 4
## 193 No 14 3 3
## 194 No 14 3 1
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## 196 No 11 3 2
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## 201 Yes 16 3 4
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## 204 Yes 13 3 4
## 205 No 11 3 1
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## 207 No 13 3 2
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## 209 No 14 3 4
## 210 Yes 13 3 3
## 211 No 23 4 3
## 212 No 13 3 2
## 213 No 17 3 1
## 214 Yes 13 3 4
## 215 No 13 3 4
## 216 No 14 3 3
## 217 No 19 3 3
## 218 No 11 3 2
## 219 No 14 3 3
## 220 Yes 20 4 2
## 221 No 22 4 3
## 222 No 14 3 4
## 223 Yes 25 4 3
## 224 No 14 3 4
## 225 No 19 3 4
## 226 No 13 3 3
## 227 No 12 3 3
## 228 Yes 16 3 1
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## 230 Yes 19 3 2
## 231 No 19 3 1
## 232 No 13 3 1
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## 235 Yes 12 3 4
## 236 No 11 3 3
## 237 No 11 3 1
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## 239 No 14 3 3
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## 241 Yes 21 4 4
## 242 No 22 4 3
## 243 No 23 4 4
## 244 No 12 3 1
## 245 Yes 15 3 3
## 246 No 14 3 3
## 247 Yes 14 3 3
## 248 No 14 3 2
## 249 Yes 14 3 1
## 250 No 16 3 2
## 251 No 12 3 3
## 252 No 13 3 4
## 253 No 14 3 4
## 254 No 17 3 3
## 255 No 11 3 3
## 256 No 13 3 3
## 257 No 11 3 3
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## 260 Yes 19 3 3
## 261 No 14 3 3
## 262 No 13 3 3
## 263 Yes 13 3 3
## 264 No 15 3 2
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## 266 No 13 3 3
## 267 No 14 3 4
## 268 No 12 3 1
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## 273 No 21 4 4
## 274 No 13 3 1
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## 276 No 13 3 4
## 277 No 13 3 4
## 278 No 19 3 1
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## 284 No 12 3 4
## 285 No 13 3 4
## 286 Yes 12 3 2
## 287 Yes 23 4 1
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## 301 No 14 3 4
## 302 No 22 4 4
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## 305 No 24 4 1
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## 309 Yes 14 3 4
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## 312 No 15 3 3
## 313 No 12 3 1
## 314 No 17 3 3
## 315 No 14 3 4
## 316 Yes 17 3 3
## 317 No 18 3 4
## 318 No 12 3 2
## 319 Yes 12 3 4
## 320 Yes 12 3 4
## 321 No 20 4 3
## 322 No 12 3 4
## 323 No 12 3 2
## 324 Yes 23 4 1
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## 326 No 19 3 3
## 327 Yes 14 3 4
## 328 Yes 17 3 3
## 329 Yes 16 3 3
## 330 No 14 3 3
## 331 No 14 3 1
## 332 No 11 3 3
## 333 Yes 12 3 3
## 334 No 11 3 4
## 335 No 21 4 4
## 336 No 22 4 2
## 337 No 11 3 4
## 338 No 17 3 3
## 339 Yes 14 3 4
## 340 No 13 3 4
## 341 Yes 14 3 4
## 342 Yes 12 3 2
## 343 No 15 3 3
## 344 No 20 4 3
## 345 No 19 3 3
## 346 No 12 3 1
## 347 No 11 3 4
## 348 No 20 4 1
## 349 No 13 3 4
## 350 No 11 3 3
## 351 No 13 3 2
## 352 No 11 3 2
## 353 Yes 13 3 4
## 354 Yes 18 3 3
## 355 Yes 13 3 4
## 356 No 18 3 4
## 357 No 19 3 3
## 358 No 16 3 2
## 359 Yes 22 4 2
## 360 No 11 3 3
## 361 No 14 3 1
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## 363 No 15 3 4
## 364 No 19 3 4
## 365 No 14 3 4
## 366 No 11 3 4
## 367 No 13 3 3
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## 369 No 16 3 4
## 370 No 16 3 3
## 371 No 12 3 3
## 372 No 11 3 3
## 373 No 18 3 1
## 374 No 11 3 1
## 375 No 11 3 1
## 376 Yes 13 3 4
## 377 No 16 3 3
## 378 Yes 12 3 3
## 379 Yes 18 3 1
## 380 Yes 11 3 2
## 381 No 11 3 2
## 382 No 23 4 4
## 383 No 11 3 1
## 384 No 13 3 3
## 385 Yes 12 3 3
## 386 No 13 3 2
## 387 No 14 3 1
## 388 No 12 3 2
## 389 No 15 3 3
## 390 Yes 11 3 3
## 391 No 17 3 2
## 392 Yes 19 3 3
## 393 Yes 13 3 4
## 394 No 19 3 4
## 395 Yes 14 3 4
## 396 Yes 18 3 2
## 397 No 13 3 3
## 398 No 15 3 4
## 399 No 21 4 1
## 400 Yes 18 3 2
## 401 No 20 4 3
## 402 No 16 3 2
## 403 No 12 3 3
## 404 No 12 3 3
## 405 No 11 3 4
## 406 Yes 14 3 3
## 407 No 14 3 1
## 408 No 14 3 3
## 409 No 16 3 3
## 410 No 24 4 3
## 411 No 21 4 3
## 412 No 12 3 2
## 413 No 21 4 1
## 414 No 13 3 3
## 415 No 16 3 4
## 416 No 14 3 3
## 417 No 14 3 3
## 418 No 18 3 4
## 419 No 13 3 2
## 420 No 18 3 3
## 421 No 15 3 3
## 422 Yes 15 3 1
## 423 Yes 18 3 2
## 424 No 15 3 3
## 425 No 11 3 3
## 426 No 13 3 3
## 427 No 13 3 2
## 428 No 17 3 1
## 429 Yes 14 3 4
## 430 No 13 3 2
## 431 Yes 11 3 1
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## 433 No 20 4 1
## 434 No 13 3 3
## 435 No 12 3 2
## 436 No 18 3 1
## 437 No 17 3 4
## 438 No 13 3 4
## 439 Yes 14 3 3
## 440 No 13 3 3
## 441 Yes 14 3 4
## 442 No 17 3 3
## 443 No 11 3 3
## 444 Yes 18 3 1
## 445 Yes 17 3 3
## 446 No 12 3 3
## 447 No 15 3 4
## 448 No 18 3 3
## 449 No 11 3 4
## 450 No 15 3 1
## 451 Yes 13 3 1
## 452 No 13 3 3
## 453 Yes 18 3 4
## 454 No 14 3 2
## 455 No 11 3 1
## 456 No 14 3 2
## 457 No 11 3 1
## 458 Yes 22 4 4
## 459 No 22 4 4
## 460 No 16 3 3
## 461 No 17 3 4
## 462 No 11 3 3
## 463 No 11 3 4
## 464 No 20 4 4
## 465 Yes 16 3 4
## 466 Yes 11 3 3
## 467 Yes 21 4 4
## 468 No 12 3 2
## 469 No 18 3 4
## 470 No 11 3 3
## 471 No 11 3 3
## 472 Yes 24 4 2
## 473 Yes 14 3 2
## 474 No 13 3 2
## 475 No 16 3 4
## 476 Yes 21 4 3
## 477 No 19 3 4
## 478 No 12 3 1
## 479 No 13 3 3
## 480 Yes 25 4 1
## 481 No 12 3 1
## 482 Yes 19 3 2
## 483 Yes 15 3 2
## 484 Yes 18 3 3
## 485 No 12 3 4
## 486 Yes 11 3 1
## 487 No 13 3 3
## 488 No 17 3 4
## 489 Yes 13 3 4
## 490 No 11 3 4
## 491 Yes 11 3 4
## 492 Yes 11 3 2
## 493 No 11 3 3
## 494 No 19 3 4
## 495 Yes 13 3 1
## 496 No 14 3 3
## 497 No 21 4 4
## 498 Yes 12 3 3
## 499 No 13 3 2
## 500 No 19 3 3
## 501 Yes 14 3 4
## 502 Yes 14 3 2
## 503 Yes 11 3 3
## 504 No 11 3 3
## 505 No 19 3 2
## 506 No 12 3 1
## 507 Yes 13 3 2
## 508 No 11 3 2
## 509 No 17 3 3
## 510 No 12 3 4
## 511 No 11 3 3
## 512 No 21 4 2
## 513 No 13 3 1
## 514 Yes 12 3 3
## 515 No 24 4 3
## 516 No 18 3 4
## 517 Yes 18 3 3
## 518 No 22 4 2
## 519 No 12 3 1
## 520 No 16 3 4
## 521 No 16 3 3
## 522 Yes 17 3 3
## 523 No 17 3 3
## 524 Yes 17 3 3
## 525 No 17 3 1
## 526 No 12 3 1
## 527 Yes 11 3 1
## 528 Yes 12 3 4
## 529 No 18 3 1
## 530 Yes 18 3 1
## 531 No 18 3 1
## 532 No 13 3 1
## 533 No 24 4 3
## 534 No 18 3 2
## 535 Yes 11 3 4
## 536 No 14 3 4
## 537 No 16 3 3
## 538 Yes 11 3 3
## 539 Yes 16 3 2
## 540 No 13 3 4
## 541 No 13 3 1
## 542 No 19 3 2
## 543 Yes 15 3 4
## 544 No 14 3 2
## 545 No 11 3 3
## 546 No 12 3 2
## 547 No 11 3 3
## 548 No 14 3 4
## 549 No 17 3 3
## 550 No 16 3 1
## 551 No 12 3 2
## 552 No 13 3 4
## 553 No 21 4 4
## 554 No 17 3 1
## 555 No 14 3 4
## 556 Yes 11 3 3
## 557 No 11 3 3
## 558 Yes 13 3 4
## 559 No 13 3 4
## 560 No 17 3 2
## 561 No 12 3 3
## 562 Yes 14 3 2
## 563 No 14 3 2
## 564 No 20 4 3
## 565 No 21 4 3
## 566 Yes 12 3 3
## 567 No 13 3 4
## 568 No 11 3 4
## 569 No 17 3 1
## 570 Yes 18 3 4
## 571 No 21 4 3
## 572 No 19 3 1
## 573 Yes 12 3 3
## 574 No 21 4 3
## 575 No 20 4 4
## 576 No 18 3 3
## 577 No 14 3 1
## 578 No 14 3 4
## 579 No 13 3 4
## 580 Yes 11 3 2
## 581 No 11 3 3
## 582 No 14 3 2
## 583 Yes 16 3 3
## 584 Yes 20 4 1
## 585 Yes 13 3 4
## 586 Yes 15 3 3
## 587 Yes 12 3 4
## 588 No 23 4 2
## 589 No 18 3 1
## 590 Yes 15 3 3
## 591 Yes 14 3 2
## 592 Yes 23 4 3
## 593 No 15 3 4
## 594 No 14 3 4
## 595 No 13 3 4
## 596 No 22 4 1
## 597 No 16 3 1
## 598 No 22 4 2
## 599 Yes 20 4 2
## 600 Yes 16 3 1
## 601 No 13 3 3
## 602 No 14 3 2
## 603 Yes 12 3 3
## 604 No 13 3 1
## 605 No 13 3 3
## 606 No 19 3 3
## 607 No 11 3 4
## 608 Yes 24 4 3
## 609 No 12 3 4
## 610 No 19 3 4
## 611 No 12 3 2
## 612 Yes 17 3 1
## 613 No 20 4 1
## 614 Yes 13 3 2
## 615 No 11 3 1
## 616 No 14 3 3
## 617 No 11 3 4
## 618 Yes 21 4 3
## 619 No 15 3 1
## 620 No 18 3 3
## 621 Yes 18 3 2
## 622 Yes 19 3 3
## 623 No 16 3 3
## 624 Yes 16 3 3
## 625 No 11 3 3
## 626 No 11 3 2
## 627 Yes 12 3 3
## 628 Yes 14 3 2
## 629 No 18 3 2
## 630 No 14 3 2
## 631 No 20 4 2
## 632 No 21 4 1
## 633 Yes 12 3 3
## 634 No 22 4 1
## 635 No 16 3 1
## 636 No 13 3 2
## 637 No 17 3 4
## 638 No 18 3 3
## 639 Yes 11 3 2
## 640 No 11 3 2
## 641 No 22 4 2
## 642 No 12 3 2
## 643 No 15 3 2
## 644 Yes 23 4 2
## 645 No 17 3 1
## 646 No 11 3 1
## 647 No 12 3 4
## 648 No 15 3 3
## 649 No 14 3 2
## 650 Yes 14 3 2
## 651 No 19 3 2
## 652 Yes 18 3 2
## 653 No 17 3 3
## 654 No 21 4 2
## 655 No 16 3 3
## 656 Yes 13 3 3
## 657 No 16 3 4
## 658 No 20 4 1
## 659 Yes 15 3 4
## 660 No 13 3 3
## 661 No 15 3 4
## 662 No 12 3 3
## 663 Yes 12 3 2
## 664 No 20 4 1
## 665 No 20 4 3
## 666 Yes 12 3 4
## 667 No 14 3 4
## 668 No 13 3 1
## 669 No 18 3 1
## 670 No 14 3 1
## 671 No 13 3 1
## 672 No 14 3 3
## 673 No 14 3 4
## 674 Yes 19 3 1
## 675 No 17 3 1
## 676 No 14 3 3
## 677 No 15 3 1
## 678 No 11 3 1
## 679 No 13 3 2
## 680 No 15 3 2
## 681 No 12 3 1
## 682 No 20 4 1
## 683 No 14 3 2
## 684 Yes 16 3 2
## 685 No 11 3 3
## 686 No 25 4 4
## 687 Yes 12 3 1
## 688 No 21 4 4
## 689 No 22 4 3
## 690 No 13 3 4
## 691 No 11 3 3
## 692 Yes 12 3 4
## 693 No 23 4 2
## 694 Yes 23 4 3
## 695 No 14 3 3
## 696 Yes 18 3 1
## 697 No 25 4 2
## 698 No 16 3 4
## 699 Yes 12 3 3
## 700 No 11 3 1
## 701 No 11 3 4
## 702 No 20 4 1
## 703 Yes 11 3 2
## 704 Yes 11 3 3
## 705 No 17 3 4
## 706 No 14 3 4
## 707 No 15 3 2
## 708 No 18 3 4
## 709 No 11 3 2
## 710 No 11 3 4
## 711 Yes 23 4 4
## 712 No 12 3 3
## 713 No 11 3 3
## 714 No 11 3 1
## 715 No 15 3 2
## 716 No 11 3 3
## 717 No 18 3 1
## 718 No 13 3 4
## 719 Yes 24 4 1
## 720 No 17 3 2
## 721 No 15 3 3
## 722 No 17 3 1
## 723 No 14 3 3
## 724 No 11 3 2
## 725 No 13 3 4
## 726 No 12 3 4
## 727 No 15 3 2
## 728 Yes 17 3 4
## 729 Yes 18 3 2
## 730 No 14 3 3
## 731 No 21 4 3
## 732 Yes 18 3 1
## 733 No 12 3 1
## 734 No 12 3 4
## 735 No 13 3 3
## 736 No 14 3 2
## 737 No 11 3 1
## 738 No 17 3 4
## 739 No 16 3 2
## 740 No 14 3 4
## 741 No 18 3 4
## 742 No 21 4 3
## 743 No 17 3 1
## 744 No 13 3 3
## 745 Yes 22 4 2
## 746 No 14 3 2
## 747 Yes 15 3 4
## 748 No 20 4 3
## 749 No 13 3 2
## 750 No 14 3 1
## 751 Yes 14 3 2
## 752 Yes 11 3 1
## 753 Yes 15 3 3
## 754 No 21 4 3
## 755 No 12 3 1
## 756 No 14 3 3
## 757 Yes 25 4 4
## 758 No 14 3 2
## 759 No 19 3 4
## 760 Yes 16 3 1
## 761 Yes 20 4 3
## 762 Yes 11 3 3
## 763 No 13 3 1
## 764 No 12 3 1
## 765 No 19 3 3
## 766 Yes 14 3 1
## 767 No 13 3 3
## 768 No 15 3 3
## 769 No 11 3 3
## 770 No 13 3 4
## 771 No 14 3 3
## 772 No 25 4 3
## 773 No 25 4 4
## 774 Yes 17 3 1
## 775 Yes 14 3 3
## 776 No 18 3 4
## 777 No 21 4 1
## 778 No 12 3 3
## 779 No 20 4 3
## 780 Yes 14 3 2
## 781 Yes 22 4 4
## 782 No 11 3 1
## 783 Yes 11 3 3
## 784 No 12 3 1
## 785 No 11 3 4
## 786 No 11 3 3
## 787 No 11 3 4
## 788 No 15 3 1
## 789 No 12 3 2
## 790 No 18 3 4
## 791 No 25 4 1
## 792 No 15 3 4
## 793 Yes 19 3 4
## 794 No 13 3 2
## 795 Yes 15 3 1
## 796 Yes 11 3 1
## 797 Yes 14 3 2
## 798 Yes 17 3 3
## 799 Yes 25 4 2
## 800 No 20 4 1
## 801 No 19 3 1
## 802 Yes 13 3 3
## 803 Yes 14 3 4
## 804 Yes 22 4 1
## 805 No 12 3 3
## 806 Yes 13 3 4
## 807 No 11 3 2
## 808 No 24 4 1
## 809 No 12 3 2
## 810 Yes 16 3 4
## 811 No 11 3 3
## 812 No 11 3 2
## 813 Yes 12 3 4
## 814 No 11 3 3
## 815 No 19 3 4
## 816 No 13 3 2
## 817 Yes 24 4 2
## 818 No 11 3 4
## 819 No 13 3 4
## 820 Yes 11 3 4
## 821 No 17 3 4
## 822 No 14 3 4
## 823 No 21 4 4
## 824 No 12 3 3
## 825 Yes 12 3 2
## 826 No 17 3 4
## 827 No 13 3 1
## 828 Yes 12 3 4
## 829 No 14 3 2
## 830 No 11 3 3
## 831 Yes 15 3 3
## 832 Yes 19 3 4
## 833 No 12 3 3
## 834 No 25 4 3
## 835 Yes 15 3 4
## 836 No 13 3 4
## 837 Yes 15 3 4
## 838 No 12 3 4
## 839 Yes 12 3 3
## 840 No 12 3 4
## 841 No 13 3 3
## 842 No 13 3 1
## 843 No 22 4 3
## 844 No 11 3 4
## 845 No 12 3 1
## 846 No 12 3 4
## 847 No 14 3 4
## 848 No 17 3 1
## 849 Yes 12 3 1
## 850 No 18 3 1
## 851 Yes 11 3 3
## 852 No 13 3 1
## 853 Yes 12 3 4
## 854 No 19 3 3
## 855 Yes 11 3 4
## 856 No 13 3 1
## 857 Yes 11 3 3
## 858 No 24 4 1
## 859 No 21 4 4
## 860 No 11 3 4
## 861 No 14 3 2
## 862 No 14 3 4
## 863 Yes 13 3 2
## 864 Yes 12 3 4
## 865 Yes 18 3 4
## 866 No 11 3 3
## 867 No 18 3 1
## 868 Yes 17 3 3
## 869 Yes 19 3 3
## 870 No 11 3 4
## 871 No 17 3 4
## 872 No 13 3 4
## 873 No 20 4 3
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## 876 No 20 4 3
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## 878 No 13 3 4
## 879 No 19 3 2
## 880 No 12 3 1
## 881 Yes 14 3 4
## 882 No 17 3 2
## 883 No 15 3 4
## 884 No 16 3 4
## 885 Yes 17 3 1
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## 887 No 13 3 4
## 888 No 14 3 2
## 889 No 16 3 3
## 890 No 18 3 1
## 891 No 13 3 3
## 892 No 22 4 3
## 893 Yes 19 3 4
## 894 No 14 3 4
## 895 Yes 18 3 2
## 896 No 14 3 4
## 897 No 19 3 2
## 898 No 11 3 3
## 899 No 16 3 3
## 900 No 17 3 1
## 901 Yes 14 3 3
## 902 No 14 3 4
## 903 No 18 3 4
## 904 No 16 3 2
## 905 No 13 3 1
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## 907 No 14 3 4
## 908 No 12 3 3
## 909 No 15 3 1
## 910 No 12 3 3
## 911 Yes 20 4 2
## 912 No 18 3 3
## 913 Yes 14 3 3
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## 915 Yes 20 4 2
## 916 No 15 3 4
## 917 No 13 3 4
## 918 No 14 3 1
## 919 No 14 3 1
## 920 No 13 3 3
## 921 No 11 3 1
## 922 Yes 11 3 3
## 923 No 12 3 2
## 924 No 11 3 3
## 925 No 14 3 3
## 926 Yes 14 3 3
## 927 Yes 13 3 3
## 928 No 15 3 3
## 929 No 11 3 1
## 930 Yes 24 4 3
## 931 No 14 3 4
## 932 No 14 3 4
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## 934 Yes 12 3 2
## 935 No 25 4 2
## 936 No 15 3 3
## 937 No 18 3 3
## 938 No 13 3 1
## 939 No 13 3 3
## 940 Yes 16 3 4
## 941 No 19 3 3
## 942 No 25 4 2
## 943 Yes 11 3 2
## 944 Yes 22 4 3
## 945 No 13 3 2
## 946 No 11 3 2
## 947 No 11 3 3
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## 949 No 12 3 3
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## 951 No 13 3 1
## 952 No 11 3 4
## 953 No 16 3 1
## 954 No 11 3 4
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## 956 Yes 11 3 2
## 957 No 11 3 3
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## 960 No 17 3 3
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## 963 No 11 3 3
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## 975 No 18 3 3
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## 978 Yes 11 3 4
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## 983 No 13 3 3
## 984 No 18 3 1
## 985 Yes 13 3 3
## 986 No 18 3 3
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## 989 No 11 3 3
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## 999 No 11 3 3
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## 1007 Yes 17 3 1
## 1008 No 14 3 2
## 1009 No 19 3 4
## 1010 No 15 3 3
## 1011 No 18 3 3
## 1012 No 13 3 2
## 1013 Yes 15 3 4
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## 1016 No 18 3 3
## 1017 No 19 3 1
## 1018 No 12 3 2
## 1019 No 21 4 4
## 1020 No 21 4 3
## 1021 No 13 3 3
## 1022 No 17 3 4
## 1023 No 22 4 4
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## 1027 No 20 4 3
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## 1032 No 14 3 2
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## 1035 Yes 13 3 2
## 1036 No 16 3 4
## 1037 Yes 24 4 1
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## 1039 Yes 11 3 3
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## 1041 No 13 3 3
## 1042 No 12 3 3
## 1043 No 19 3 1
## 1044 No 12 3 3
## 1045 No 11 3 2
## 1046 No 23 4 4
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## 1048 No 18 3 4
## 1049 Yes 12 3 1
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## 1051 No 14 3 1
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## 1053 No 15 3 4
## 1054 No 11 3 3
## 1055 No 11 3 3
## 1056 No 20 4 4
## 1057 No 11 3 3
## 1058 No 12 3 1
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## 1060 No 11 3 1
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## 1063 No 11 3 4
## 1064 No 16 3 1
## 1065 No 11 3 3
## 1066 No 17 3 3
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## 1069 Yes 11 3 2
## 1070 No 11 3 3
## 1071 No 17 3 2
## 1072 No 11 3 3
## 1073 No 14 3 3
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## 1075 Yes 20 4 4
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## 1078 No 15 3 4
## 1079 No 12 3 2
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## 1090 No 20 4 1
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## 1092 No 14 3 4
## 1093 No 11 3 4
## 1094 No 13 3 3
## 1095 No 22 4 1
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## 1097 Yes 11 3 2
## 1098 Yes 14 3 4
## 1099 Yes 22 4 3
## 1100 No 12 3 3
## 1101 Yes 12 3 4
## 1102 Yes 18 3 4
## 1103 No 14 3 4
## 1104 No 15 3 3
## 1105 Yes 11 3 3
## 1106 No 13 3 3
## 1107 No 11 3 4
## 1108 No 13 3 3
## 1109 Yes 14 3 3
## 1110 No 23 4 4
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## 1112 No 14 3 2
## 1113 Yes 11 3 4
## 1114 Yes 16 3 4
## 1115 No 11 3 1
## 1116 Yes 13 3 1
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## 1118 Yes 13 3 3
## 1119 No 11 3 1
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## 1121 Yes 20 4 2
## 1122 No 22 4 2
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## 1124 No 11 3 3
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## 1134 No 12 3 4
## 1135 No 18 3 4
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## 1165 No 21 4 1
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## 1167 No 16 3 1
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## 1169 No 11 3 3
## 1170 Yes 22 4 1
## StandardHours StockOptionLevel TotalWorkingYears
## 1 80 1 8
## 2 80 0 21
## 3 80 0 10
## 4 80 2 14
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## 1094 18 15 15
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## 1096 7 0 0
## 1097 6 1 1
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## 1099 3 0 2
## 1100 7 12 17
## 1101 0 0 0
## 1102 3 0 3
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## 1104 4 4 3
## 1105 7 0 8
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## 1110 9 7 7
## 1111 2 1 2
## 1112 7 0 7
## 1113 1 7 7
## 1114 4 0 1
## 1115 7 2 13
## 1116 5 1 2
## 1117 2 2 2
## 1118 2 0 2
## 1119 8 4 7
## 1120 2 2 2
## 1121 2 2 2
## 1122 16 5 10
## 1123 6 12 6
## 1124 0 0 0
## 1125 9 4 9
## 1126 3 1 3
## 1127 2 0 2
## 1128 4 1 7
## 1129 7 7 4
## 1130 7 11 16
## 1131 7 0 0
## 1132 8 9 6
## 1133 8 6 0
## 1134 3 0 1
## 1135 2 1 3
## 1136 4 0 3
## 1137 3 1 2
## 1138 7 5 7
## 1139 0 0 0
## 1140 10 10 1
## 1141 2 0 3
## 1142 0 0 0
## 1143 4 1 7
## 1144 0 0 0
## 1145 0 0 0
## 1146 2 1 2
## 1147 7 0 7
## 1148 9 0 9
## 1149 3 0 2
## 1150 0 0 0
## 1151 1 0 3
## 1152 7 2 8
## 1153 4 7 0
## 1154 4 0 3
## 1155 3 0 0
## 1156 4 1 2
## 1157 7 1 0
## 1158 3 1 2
## 1159 0 0 0
## 1160 2 2 2
## 1161 2 2 3
## 1162 3 4 3
## 1163 4 1 1
## 1164 6 0 8
## 1165 0 0 0
## 1166 8 3 9
## 1167 8 5 7
## 1168 0 0 1
## 1169 0 1 7
## 1170 6 7 7allData <- rbind.fill(allData, ValidationNoSalary)
allData## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## 7 7 41 No Travel_Rarely 1283 Research & Development
## 8 8 37 No Travel_Rarely 309 Sales
## 9 9 34 No Travel_Rarely 1333 Sales
## 10 10 34 No Travel_Frequently 653 Research & Development
## 11 11 43 No Travel_Rarely 823 Research & Development
## 12 12 28 No Non-Travel 280 Human Resources
## 13 13 35 No Travel_Rarely 950 Research & Development
## 14 14 30 No Travel_Rarely 202 Sales
## 15 15 46 No Travel_Rarely 991 Human Resources
## 16 16 31 No Non-Travel 1188 Sales
## 17 17 32 No Travel_Rarely 498 Research & Development
## 18 18 46 No Non-Travel 1144 Research & Development
## 19 19 34 No Travel_Rarely 181 Research & Development
## 20 20 44 No Travel_Rarely 170 Research & Development
## 21 21 36 No Travel_Rarely 913 Research & Development
## 22 22 48 No Travel_Rarely 817 Sales
## 23 23 43 No Travel_Frequently 1001 Research & Development
## 24 24 31 No Travel_Frequently 715 Sales
## 25 25 33 No Travel_Rarely 1069 Research & Development
## 26 26 44 No Travel_Rarely 1099 Sales
## 27 27 38 No Travel_Rarely 1245 Sales
## 28 28 33 Yes Travel_Rarely 603 Sales
## 29 29 53 Yes Travel_Rarely 1168 Sales
## 30 30 52 No Non-Travel 715 Research & Development
## 31 31 32 No Travel_Frequently 689 Sales
## 32 32 33 No Travel_Rarely 267 Research & Development
## 33 33 46 No Travel_Rarely 488 Sales
## 34 34 35 No Travel_Rarely 982 Research & Development
## 35 35 32 No Travel_Rarely 1373 Research & Development
## 36 36 32 No Travel_Frequently 1311 Research & Development
## 37 37 35 Yes Travel_Rarely 737 Sales
## 38 38 36 Yes Travel_Rarely 1456 Sales
## 39 39 56 No Travel_Rarely 206 Human Resources
## 40 40 31 No Travel_Frequently 793 Sales
## 41 41 58 No Non-Travel 350 Sales
## 42 42 45 No Travel_Rarely 954 Sales
## 43 43 49 No Travel_Rarely 464 Research & Development
## 44 44 42 No Travel_Rarely 1059 Research & Development
## 45 45 23 No Travel_Rarely 373 Research & Development
## 46 46 42 No Travel_Frequently 748 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 48 48 38 No Travel_Rarely 770 Sales
## 49 49 18 Yes Travel_Frequently 1306 Sales
## 50 50 27 No Non-Travel 691 Research & Development
## 51 51 38 No Travel_Rarely 362 Research & Development
## 52 52 58 No Travel_Rarely 1055 Research & Development
## 53 53 44 No Travel_Rarely 1117 Research & Development
## 54 54 45 No Non-Travel 805 Research & Development
## 55 55 31 No Travel_Rarely 746 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
## 57 57 42 Yes Travel_Frequently 933 Research & Development
## 58 58 29 No Travel_Rarely 1370 Research & Development
## 59 59 37 No Non-Travel 728 Research & Development
## 60 60 25 Yes Travel_Frequently 599 Sales
## 61 61 36 Yes Travel_Rarely 530 Sales
## 62 62 36 No Travel_Rarely 325 Research & Development
## 63 63 31 No Travel_Rarely 1154 Sales
## 64 64 18 No Non-Travel 1431 Research & Development
## 65 65 24 Yes Travel_Rarely 984 Research & Development
## 66 66 32 No Travel_Rarely 334 Research & Development
## 67 67 35 No Travel_Rarely 1017 Research & Development
## 68 68 23 No Travel_Rarely 571 Research & Development
## 69 69 35 No Travel_Frequently 664 Research & Development
## 70 70 37 No Travel_Rarely 1372 Research & Development
## 71 71 41 No Travel_Rarely 642 Research & Development
## 72 72 28 No Travel_Rarely 866 Sales
## 73 73 35 Yes Travel_Rarely 556 Research & Development
## 74 74 41 No Travel_Rarely 167 Research & Development
## 75 75 52 Yes Travel_Rarely 1030 Sales
## 76 76 30 No Travel_Frequently 721 Research & Development
## 77 77 34 No Travel_Rarely 971 Sales
## 78 78 46 No Travel_Rarely 563 Sales
## 79 79 45 No Non-Travel 1052 Sales
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 81 81 37 Yes Travel_Rarely 1141 Research & Development
## 82 82 35 No Travel_Rarely 1370 Research & Development
## 83 83 38 No Travel_Frequently 508 Research & Development
## 84 84 37 No Travel_Rarely 1319 Research & Development
## 85 85 36 No Travel_Rarely 429 Research & Development
## 86 86 59 No Travel_Rarely 715 Research & Development
## 87 87 47 No Travel_Rarely 703 Sales
## 88 88 35 Yes Travel_Rarely 622 Research & Development
## 89 89 27 No Travel_Frequently 994 Sales
## 90 90 46 No Travel_Rarely 705 Sales
## 91 91 38 No Travel_Frequently 1490 Research & Development
## 92 92 29 Yes Travel_Rarely 408 Sales
## 93 93 37 Yes Travel_Rarely 1373 Research & Development
## 94 94 35 No Travel_Rarely 670 Research & Development
## 95 95 50 No Travel_Rarely 1452 Research & Development
## 96 96 42 No Travel_Rarely 635 Sales
## 97 97 46 No Travel_Rarely 228 Sales
## 98 98 37 No Non-Travel 1040 Research & Development
## 99 99 29 No Travel_Frequently 410 Research & Development
## 100 100 34 No Travel_Rarely 401 Research & Development
## 101 101 27 No Travel_Rarely 1377 Research & Development
## 102 102 31 No Travel_Rarely 326 Sales
## 103 103 34 No Non-Travel 1381 Sales
## 104 104 23 No Travel_Rarely 885 Research & Development
## 105 105 53 No Travel_Rarely 447 Research & Development
## 106 106 26 No Travel_Rarely 1384 Research & Development
## 107 107 29 No Non-Travel 1496 Research & Development
## 108 108 55 No Travel_Rarely 189 Human Resources
## 109 109 46 Yes Travel_Rarely 261 Research & Development
## 110 110 32 No Travel_Rarely 529 Research & Development
## 111 111 28 No Travel_Frequently 773 Research & Development
## 112 112 52 No Travel_Rarely 1053 Research & Development
## 113 113 52 No Travel_Rarely 994 Research & Development
## 114 114 35 No Non-Travel 1225 Research & Development
## 115 115 35 No Travel_Rarely 1361 Sales
## 116 116 37 No Non-Travel 1252 Sales
## 117 117 25 No Travel_Rarely 1356 Sales
## 118 118 29 No Travel_Rarely 1378 Research & Development
## 119 119 41 No Travel_Rarely 465 Research & Development
## 120 120 34 No Travel_Rarely 404 Research & Development
## 121 121 36 No Non-Travel 427 Research & Development
## 122 122 21 No Travel_Rarely 501 Sales
## 123 123 45 No Travel_Rarely 930 Sales
## 124 124 38 No Travel_Rarely 243 Sales
## 125 125 29 No Travel_Frequently 442 Sales
## 126 126 35 No Travel_Rarely 1224 Sales
## 127 127 32 Yes Travel_Frequently 1125 Research & Development
## 128 128 45 No Travel_Rarely 556 Research & Development
## 129 129 43 No Travel_Rarely 930 Research & Development
## 130 130 46 No Travel_Rarely 1319 Sales
## 131 131 29 No Travel_Rarely 991 Sales
## 132 132 49 No Travel_Rarely 1313 Sales
## 133 133 42 No Travel_Frequently 1368 Research & Development
## 134 134 39 No Travel_Rarely 492 Research & Development
## 135 135 43 No Travel_Frequently 559 Research & Development
## 136 136 40 No Travel_Frequently 791 Research & Development
## 137 137 29 Yes Travel_Rarely 805 Research & Development
## 138 138 54 No Travel_Rarely 1441 Research & Development
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 140 140 37 No Travel_Rarely 1189 Sales
## 141 141 56 No Travel_Rarely 1369 Research & Development
## 142 142 58 No Travel_Rarely 848 Research & Development
## 143 143 46 No Travel_Rarely 566 Research & Development
## 144 144 37 No Travel_Rarely 228 Sales
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 146 146 29 No Non-Travel 746 Sales
## 147 147 33 No Travel_Rarely 931 Research & Development
## 148 148 33 No Travel_Rarely 1242 Sales
## 149 149 24 No Travel_Rarely 477 Research & Development
## 150 150 28 No Travel_Rarely 580 Research & Development
## 151 151 31 Yes Travel_Rarely 1365 Sales
## 152 152 32 No Travel_Rarely 929 Sales
## 153 153 32 No Travel_Rarely 906 Sales
## 154 154 41 No Travel_Rarely 447 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 156 156 30 No Non-Travel 641 Sales
## 157 157 40 No Travel_Rarely 444 Sales
## 158 158 39 Yes Travel_Frequently 203 Research & Development
## 159 159 43 No Travel_Frequently 1082 Research & Development
## 160 160 42 No Travel_Frequently 458 Research & Development
## 161 161 30 No Travel_Rarely 852 Research & Development
## 162 162 28 Yes Non-Travel 1366 Research & Development
## 163 163 33 No Travel_Rarely 1216 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 165 165 35 No Travel_Rarely 1232 Sales
## 166 166 20 Yes Travel_Rarely 500 Sales
## 167 167 49 No Travel_Rarely 301 Research & Development
## 168 168 33 No Travel_Rarely 922 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 170 170 29 No Travel_Rarely 1107 Research & Development
## 171 171 30 No Travel_Rarely 501 Sales
## 172 172 47 No Non-Travel 1169 Research & Development
## 173 173 46 No Travel_Frequently 1034 Research & Development
## 174 174 33 No Travel_Rarely 536 Sales
## 175 175 53 No Travel_Rarely 1376 Sales
## 176 176 21 No Travel_Rarely 984 Research & Development
## 177 177 55 Yes Travel_Rarely 725 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 179 179 32 No Travel_Frequently 967 Sales
## 180 180 38 No Travel_Rarely 702 Sales
## 181 181 35 No Travel_Rarely 819 Research & Development
## 182 182 38 No Travel_Rarely 1009 Sales
## 183 183 43 No Travel_Frequently 957 Research & Development
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 185 185 39 No Travel_Frequently 443 Research & Development
## 186 186 28 No Travel_Rarely 1158 Research & Development
## 187 187 25 No Travel_Frequently 772 Research & Development
## 188 188 47 No Travel_Rarely 1225 Sales
## 189 189 50 No Travel_Rarely 797 Research & Development
## 190 190 28 No Non-Travel 120 Sales
## 191 191 30 No Travel_Rarely 1275 Research & Development
## 192 192 31 No Travel_Frequently 1125 Sales
## 193 193 46 No Travel_Rarely 1485 Research & Development
## 194 194 48 No Travel_Rarely 163 Sales
## 195 195 26 No Travel_Rarely 683 Research & Development
## 196 196 51 No Travel_Rarely 833 Research & Development
## 197 197 51 No Travel_Rarely 432 Research & Development
## 198 198 49 No Travel_Rarely 174 Sales
## 199 199 44 No Travel_Rarely 477 Research & Development
## 200 200 24 No Travel_Rarely 823 Research & Development
## 201 201 40 Yes Non-Travel 1479 Sales
## 202 202 32 No Travel_Rarely 548 Research & Development
## 203 203 32 No Travel_Rarely 859 Research & Development
## 204 204 28 Yes Travel_Rarely 654 Research & Development
## 205 205 32 No Travel_Rarely 1401 Sales
## 206 206 44 No Travel_Rarely 661 Research & Development
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 208 208 38 Yes Travel_Rarely 1180 Research & Development
## 209 209 30 No Travel_Rarely 201 Research & Development
## 210 210 41 No Travel_Rarely 802 Sales
## 211 211 41 No Travel_Rarely 582 Research & Development
## 212 212 42 No Travel_Rarely 265 Sales
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 214 214 42 No Travel_Rarely 932 Research & Development
## 215 215 27 No Travel_Rarely 1115 Research & Development
## 216 216 40 No Travel_Rarely 896 Research & Development
## 217 217 38 No Travel_Rarely 1495 Research & Development
## 218 218 48 No Travel_Frequently 117 Research & Development
## 219 219 30 No Travel_Rarely 438 Research & Development
## 220 220 26 No Travel_Rarely 583 Research & Development
## 221 221 34 No Travel_Rarely 304 Sales
## 222 222 45 Yes Travel_Frequently 306 Sales
## 223 223 33 No Non-Travel 530 Sales
## 224 224 28 No Travel_Rarely 1423 Research & Development
## 225 225 38 No Travel_Rarely 1206 Research & Development
## 226 226 30 No Travel_Rarely 911 Research & Development
## 227 227 35 No Travel_Rarely 464 Research & Development
## 228 228 22 No Travel_Rarely 1136 Research & Development
## 229 229 24 No Travel_Rarely 1353 Sales
## 230 230 49 No Travel_Rarely 465 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 232 232 35 No Travel_Rarely 890 Sales
## 233 233 47 Yes Travel_Frequently 1093 Sales
## 234 234 54 No Travel_Rarely 1147 Sales
## 235 235 43 No Travel_Frequently 394 Sales
## 236 236 37 Yes Travel_Rarely 625 Sales
## 237 237 42 No Travel_Rarely 300 Research & Development
## 238 238 32 No Travel_Rarely 646 Research & Development
## 239 239 25 No Travel_Rarely 309 Human Resources
## 240 240 50 No Travel_Rarely 813 Research & Development
## 241 241 42 No Travel_Rarely 319 Research & Development
## 242 242 45 No Travel_Frequently 364 Research & Development
## 243 243 29 No Travel_Rarely 1328 Research & Development
## 244 244 25 Yes Travel_Rarely 383 Sales
## 245 245 29 No Travel_Rarely 718 Research & Development
## 246 246 29 No Travel_Rarely 1086 Research & Development
## 247 247 31 No Travel_Rarely 1232 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 249 249 41 No Travel_Frequently 1200 Research & Development
## 250 250 41 No Travel_Rarely 1276 Sales
## 251 251 33 No Non-Travel 1313 Research & Development
## 252 252 36 No Non-Travel 1229 Sales
## 253 253 45 No Travel_Rarely 252 Research & Development
## 254 254 31 Yes Travel_Rarely 359 Human Resources
## 255 255 40 No Non-Travel 218 Research & Development
## 256 256 43 No Non-Travel 343 Research & Development
## 257 257 31 No Travel_Frequently 444 Sales
## 258 258 30 No Travel_Rarely 855 Sales
## 259 259 34 No Travel_Rarely 1480 Sales
## 260 260 25 No Travel_Rarely 977 Research & Development
## 261 261 31 Yes Travel_Frequently 703 Sales
## 262 262 29 Yes Travel_Rarely 224 Research & Development
## 263 263 33 Yes Travel_Rarely 813 Research & Development
## 264 264 29 No Travel_Frequently 1404 Sales
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 266 266 35 No Travel_Rarely 809 Research & Development
## 267 267 34 No Travel_Frequently 829 Research & Development
## 268 268 32 Yes Travel_Rarely 1259 Research & Development
## 269 269 33 No Travel_Frequently 1303 Research & Development
## 270 270 30 No Travel_Rarely 1176 Research & Development
## 271 271 45 No Non-Travel 589 Sales
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 273 273 27 No Travel_Rarely 728 Sales
## 274 274 36 No Travel_Rarely 329 Sales
## 275 275 55 No Travel_Rarely 692 Research & Development
## 276 276 56 No Travel_Rarely 718 Research & Development
## 277 277 22 No Travel_Rarely 253 Research & Development
## 278 278 43 No Travel_Frequently 185 Research & Development
## 279 279 25 Yes Travel_Rarely 240 Sales
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 281 281 43 No Travel_Rarely 1179 Sales
## 282 282 41 No Travel_Rarely 263 Research & Development
## 283 283 40 No Travel_Rarely 1194 Research & Development
## 284 284 31 No Travel_Rarely 691 Sales
## 285 285 38 No Travel_Rarely 433 Human Resources
## 286 286 33 No Travel_Rarely 589 Research & Development
## 287 287 22 Yes Travel_Rarely 391 Research & Development
## 288 288 34 No Travel_Rarely 1346 Research & Development
## 289 289 36 No Travel_Frequently 1467 Sales
## 290 290 35 No Travel_Rarely 384 Sales
## 291 291 45 No Travel_Rarely 974 Research & Development
## 292 292 36 No Non-Travel 1105 Research & Development
## 293 293 27 No Travel_Rarely 894 Research & Development
## 294 294 47 No Non-Travel 1162 Research & Development
## 295 295 40 No Travel_Rarely 898 Human Resources
## 296 296 35 No Travel_Rarely 1490 Research & Development
## 297 297 34 No Travel_Frequently 1069 Research & Development
## 298 298 34 Yes Travel_Frequently 658 Research & Development
## 299 299 31 Yes Non-Travel 335 Research & Development
## 300 300 28 Yes Travel_Rarely 1157 Research & Development
## 301 301 32 No Travel_Frequently 379 Sales
## 302 302 35 No Travel_Frequently 853 Sales
## 303 303 55 No Travel_Rarely 1441 Research & Development
## 304 304 35 No Travel_Rarely 1395 Research & Development
## 305 305 58 Yes Travel_Rarely 289 Research & Development
## 306 306 24 No Non-Travel 1269 Research & Development
## 307 307 50 No Travel_Rarely 804 Research & Development
## 308 308 34 No Non-Travel 697 Research & Development
## 309 309 55 No Travel_Frequently 135 Research & Development
## 310 310 25 No Travel_Rarely 810 Sales
## 311 311 34 No Travel_Rarely 1351 Research & Development
## 312 312 40 No Travel_Rarely 302 Research & Development
## 313 313 31 Yes Travel_Frequently 561 Research & Development
## 314 314 51 No Travel_Rarely 1169 Research & Development
## 315 315 60 No Travel_Rarely 1179 Sales
## 316 316 47 No Travel_Rarely 955 Sales
## 317 317 34 No Travel_Frequently 303 Sales
## 318 318 24 No Travel_Rarely 771 Research & Development
## 319 319 39 No Travel_Rarely 1498 Sales
## 320 320 19 No Travel_Rarely 265 Research & Development
## 321 321 32 No Non-Travel 1184 Research & Development
## 322 322 34 No Travel_Rarely 1397 Research & Development
## 323 323 30 No Travel_Frequently 1012 Research & Development
## 324 324 46 No Travel_Rarely 1003 Research & Development
## 325 325 39 No Travel_Rarely 466 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 327 327 44 No Travel_Rarely 1313 Research & Development
## 328 328 42 No Travel_Rarely 557 Research & Development
## 329 329 38 No Travel_Frequently 1394 Research & Development
## 330 330 42 No Travel_Rarely 1147 Human Resources
## 331 331 49 No Travel_Rarely 809 Research & Development
## 332 332 32 Yes Travel_Rarely 1045 Sales
## 333 333 44 Yes Travel_Frequently 429 Research & Development
## 334 334 27 No Travel_Rarely 608 Research & Development
## 335 335 59 No Non-Travel 1420 Human Resources
## 336 336 33 Yes Travel_Frequently 827 Research & Development
## 337 337 35 No Travel_Frequently 1182 Sales
## 338 338 34 No Travel_Frequently 618 Research & Development
## 339 339 50 Yes Travel_Frequently 959 Sales
## 340 340 27 No Travel_Rarely 1240 Research & Development
## 341 341 36 No Travel_Rarely 852 Research & Development
## 342 342 28 No Travel_Frequently 193 Research & Development
## 343 343 33 No Travel_Rarely 654 Research & Development
## 344 344 30 No Travel_Frequently 160 Research & Development
## 345 345 30 No Travel_Rarely 1329 Sales
## 346 346 39 No Travel_Rarely 119 Sales
## 347 347 34 No Travel_Frequently 1003 Research & Development
## 348 348 26 No Travel_Frequently 1479 Research & Development
## 349 349 44 No Non-Travel 111 Research & Development
## 350 350 56 No Travel_Rarely 832 Research & Development
## 351 351 42 No Travel_Rarely 1396 Research & Development
## 352 352 31 No Travel_Rarely 218 Sales
## 353 353 40 No Travel_Frequently 902 Research & Development
## 354 354 40 No Non-Travel 1142 Research & Development
## 355 355 41 No Travel_Rarely 796 Sales
## 356 356 34 No Travel_Rarely 1326 Sales
## 357 357 26 No Travel_Rarely 841 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
## 359 359 32 No Travel_Rarely 427 Research & Development
## 360 360 36 No Travel_Rarely 1403 Research & Development
## 361 361 52 No Travel_Rarely 699 Research & Development
## 362 362 29 Yes Travel_Frequently 746 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 364 364 40 No Travel_Rarely 1416 Research & Development
## 365 365 55 No Travel_Rarely 282 Research & Development
## 366 366 56 No Travel_Frequently 906 Sales
## 367 367 34 No Travel_Frequently 135 Research & Development
## 368 368 40 No Travel_Rarely 1342 Sales
## 369 369 23 No Travel_Rarely 977 Research & Development
## 370 370 32 No Travel_Rarely 1018 Research & Development
## 371 371 31 No Travel_Frequently 853 Research & Development
## 372 372 30 No Non-Travel 1398 Sales
## 373 373 45 No Travel_Frequently 1249 Research & Development
## 374 374 37 No Travel_Rarely 1470 Research & Development
## 375 375 20 No Travel_Rarely 727 Sales
## 376 376 50 No Travel_Frequently 333 Research & Development
## 377 377 36 Yes Travel_Rarely 885 Research & Development
## 378 378 42 No Travel_Rarely 419 Sales
## 379 379 29 No Travel_Rarely 942 Research & Development
## 380 380 30 Yes Travel_Rarely 138 Research & Development
## 381 381 47 No Travel_Rarely 1482 Research & Development
## 382 382 26 No Travel_Rarely 1355 Human Resources
## 383 383 48 No Travel_Rarely 1221 Sales
## 384 384 23 No Travel_Rarely 541 Sales
## 385 385 51 Yes Travel_Rarely 1323 Research & Development
## 386 386 38 No Travel_Rarely 1333 Research & Development
## 387 387 35 No Travel_Frequently 944 Sales
## 388 388 29 No Travel_Rarely 232 Research & Development
## 389 389 36 No Travel_Rarely 530 Sales
## 390 390 21 Yes Travel_Frequently 756 Sales
## 391 391 34 No Travel_Rarely 1111 Sales
## 392 392 26 No Travel_Rarely 1443 Sales
## 393 393 31 No Non-Travel 976 Research & Development
## 394 394 60 No Travel_Frequently 1499 Sales
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 396 396 31 No Travel_Rarely 192 Research & Development
## 397 397 46 No Travel_Rarely 1009 Research & Development
## 398 398 34 Yes Travel_Rarely 1107 Human Resources
## 399 399 56 No Non-Travel 667 Research & Development
## 400 400 44 No Travel_Frequently 1193 Research & Development
## 401 401 30 No Travel_Rarely 317 Research & Development
## 402 402 42 No Non-Travel 926 Research & Development
## 403 403 47 No Travel_Rarely 207 Research & Development
## 404 404 36 No Travel_Rarely 711 Research & Development
## 405 405 35 No Travel_Rarely 1343 Research & Development
## 406 406 43 No Travel_Rarely 782 Research & Development
## 407 407 31 No Travel_Rarely 196 Sales
## 408 408 36 No Travel_Rarely 917 Research & Development
## 409 409 53 No Travel_Rarely 1219 Sales
## 410 410 43 No Travel_Rarely 589 Research & Development
## 411 411 36 No Travel_Rarely 1278 Human Resources
## 412 412 41 No Travel_Rarely 918 Sales
## 413 413 37 No Travel_Rarely 408 Research & Development
## 414 414 32 No Travel_Frequently 1318 Sales
## 415 415 34 Yes Travel_Frequently 296 Sales
## 416 416 35 No Non-Travel 1180 Research & Development
## 417 417 30 No Non-Travel 1116 Research & Development
## 418 418 36 No Travel_Frequently 566 Research & Development
## 419 419 37 No Travel_Rarely 977 Research & Development
## 420 420 29 No Travel_Rarely 986 Research & Development
## 421 421 34 No Travel_Rarely 1153 Research & Development
## 422 422 37 No Non-Travel 1063 Research & Development
## 423 423 31 Yes Travel_Rarely 249 Sales
## 424 424 18 No Non-Travel 1124 Research & Development
## 425 425 28 No Travel_Rarely 736 Sales
## 426 426 43 No Travel_Rarely 415 Sales
## 427 427 30 No Non-Travel 990 Research & Development
## 428 428 21 No Travel_Rarely 996 Research & Development
## 429 429 32 Yes Travel_Rarely 515 Research & Development
## 430 430 34 No Travel_Rarely 470 Research & Development
## 431 431 27 No Travel_Rarely 1377 Sales
## 432 432 40 No Non-Travel 458 Research & Development
## 433 433 49 Yes Travel_Frequently 1475 Research & Development
## 434 434 29 No Travel_Rarely 590 Research & Development
## 435 435 40 No Travel_Rarely 658 Sales
## 436 436 27 No Travel_Rarely 1130 Sales
## 437 437 28 Yes Travel_Rarely 890 Research & Development
## 438 438 26 No Travel_Rarely 157 Research & Development
## 439 439 50 Yes Travel_Rarely 869 Sales
## 440 440 38 No Travel_Rarely 827 Research & Development
## 441 441 32 No Travel_Frequently 1316 Research & Development
## 442 442 29 No Travel_Rarely 1082 Research & Development
## 443 443 28 Yes Travel_Rarely 529 Research & Development
## 444 444 44 No Travel_Rarely 528 Human Resources
## 445 445 46 No Travel_Rarely 734 Research & Development
## 446 446 47 No Travel_Rarely 465 Research & Development
## 447 447 38 No Travel_Frequently 1189 Research & Development
## 448 448 42 No Travel_Rarely 1128 Research & Development
## 449 449 38 No Travel_Frequently 653 Research & Development
## 450 450 45 No Non-Travel 336 Sales
## 451 451 33 No Travel_Rarely 501 Research & Development
## 452 452 44 No Travel_Rarely 1467 Research & Development
## 453 453 35 Yes Travel_Rarely 303 Sales
## 454 454 33 No Travel_Rarely 217 Sales
## 455 455 48 No Travel_Rarely 1236 Research & Development
## 456 456 40 No Travel_Rarely 611 Sales
## 457 457 31 No Travel_Rarely 1112 Sales
## 458 458 51 No Travel_Rarely 632 Sales
## 459 459 56 No Travel_Rarely 1255 Research & Development
## 460 460 36 No Travel_Rarely 172 Research & Development
## 461 461 42 No Travel_Frequently 1474 Research & Development
## 462 462 36 No Travel_Rarely 311 Research & Development
## 463 463 50 No Travel_Frequently 809 Sales
## 464 464 31 No Travel_Frequently 163 Research & Development
## 465 465 43 Yes Travel_Frequently 807 Research & Development
## 466 466 38 No Travel_Rarely 371 Research & Development
## 467 467 36 No Travel_Rarely 1157 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 469 469 26 Yes Non-Travel 265 Sales
## 470 470 53 No Travel_Rarely 1084 Research & Development
## 471 471 27 No Travel_Rarely 1354 Research & Development
## 472 472 29 Yes Travel_Frequently 459 Research & Development
## 473 473 45 No Travel_Rarely 193 Research & Development
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 475 475 28 No Travel_Rarely 895 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 477 477 37 No Travel_Rarely 342 Sales
## 478 478 26 No Travel_Rarely 775 Sales
## 479 479 52 No Travel_Rarely 258 Research & Development
## 480 480 37 No Travel_Rarely 161 Research & Development
## 481 481 29 No Travel_Rarely 136 Research & Development
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 483 483 34 No Travel_Frequently 735 Research & Development
## 484 484 56 No Travel_Rarely 713 Research & Development
## 485 485 58 Yes Travel_Rarely 147 Research & Development
## 486 486 41 Yes Travel_Rarely 1102 Sales
## 487 487 30 Yes Travel_Rarely 945 Sales
## 488 488 31 No Travel_Rarely 670 Research & Development
## 489 489 48 Yes Travel_Rarely 626 Research & Development
## 490 490 34 No Travel_Rarely 970 Research & Development
## 491 491 49 No Travel_Frequently 636 Research & Development
## 492 492 50 No Travel_Rarely 1322 Research & Development
## 493 493 29 No Travel_Rarely 469 Sales
## 494 494 35 No Travel_Rarely 1029 Research & Development
## 495 495 36 No Travel_Rarely 1266 Sales
## 496 496 51 No Travel_Rarely 1318 Sales
## 497 497 45 No Travel_Rarely 192 Research & Development
## 498 498 37 No Travel_Frequently 889 Research & Development
## 499 499 36 No Travel_Rarely 1299 Research & Development
## 500 500 39 No Non-Travel 1251 Sales
## 501 501 58 Yes Travel_Rarely 601 Research & Development
## 502 502 28 Yes Travel_Rarely 103 Research & Development
## 503 503 48 Yes Travel_Frequently 708 Sales
## 504 504 29 No Travel_Rarely 1389 Research & Development
## 505 505 47 No Travel_Rarely 359 Research & Development
## 506 506 32 No Travel_Rarely 824 Research & Development
## 507 507 33 Yes Travel_Rarely 350 Sales
## 508 508 51 No Travel_Frequently 541 Sales
## 509 509 37 No Travel_Frequently 319 Sales
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
## 511 511 38 No Travel_Rarely 345 Sales
## 512 512 39 No Non-Travel 1485 Research & Development
## 513 513 32 No Travel_Frequently 585 Research & Development
## 514 514 33 No Travel_Frequently 515 Research & Development
## 515 515 21 No Non-Travel 895 Sales
## 516 516 51 No Travel_Rarely 1302 Research & Development
## 517 517 25 Yes Travel_Rarely 867 Sales
## 518 518 42 No Travel_Rarely 855 Research & Development
## 519 519 30 No Travel_Rarely 1138 Research & Development
## 520 520 56 Yes Travel_Rarely 1162 Research & Development
## 521 521 34 No Travel_Frequently 702 Research & Development
## 522 522 28 Yes Travel_Rarely 329 Research & Development
## 523 523 45 No Travel_Rarely 1339 Research & Development
## 524 524 49 No Travel_Rarely 1245 Research & Development
## 525 525 59 No Travel_Rarely 142 Research & Development
## 526 526 53 No Travel_Frequently 124 Sales
## 527 527 45 No Travel_Rarely 1268 Sales
## 528 528 31 No Travel_Rarely 1332 Research & Development
## 529 529 35 No Travel_Rarely 1214 Research & Development
## 530 530 56 Yes Travel_Rarely 441 Research & Development
## 531 531 38 No Travel_Rarely 330 Research & Development
## 532 532 36 No Travel_Rarely 884 Sales
## 533 533 29 No Travel_Rarely 1252 Research & Development
## 534 534 32 No Non-Travel 1146 Research & Development
## 535 535 29 Yes Travel_Rarely 318 Research & Development
## 536 536 43 No Non-Travel 1344 Research & Development
## 537 537 38 No Travel_Rarely 130 Sales
## 538 538 42 No Travel_Rarely 544 Human Resources
## 539 539 24 Yes Travel_Rarely 1448 Sales
## 540 540 42 No Travel_Rarely 647 Sales
## 541 541 30 No Travel_Rarely 853 Research & Development
## 542 542 33 No Travel_Rarely 867 Research & Development
## 543 543 27 No Non-Travel 1450 Research & Development
## 544 544 38 No Travel_Rarely 1261 Research & Development
## 545 545 34 No Travel_Rarely 121 Research & Development
## 546 546 34 No Travel_Rarely 678 Research & Development
## 547 547 49 No Travel_Rarely 527 Research & Development
## 548 548 40 No Travel_Frequently 720 Research & Development
## 549 549 38 No Travel_Rarely 849 Research & Development
## 550 550 45 No Travel_Rarely 788 Human Resources
## 551 551 41 No Travel_Rarely 314 Human Resources
## 552 552 28 No Travel_Rarely 1169 Human Resources
## 553 553 36 No Travel_Frequently 1213 Human Resources
## 554 554 48 No Travel_Rarely 1469 Research & Development
## 555 555 40 No Non-Travel 1151 Research & Development
## 556 556 37 No Travel_Rarely 558 Sales
## 557 557 54 No Travel_Rarely 548 Research & Development
## 558 558 34 No Travel_Rarely 254 Research & Development
## 559 559 42 No Travel_Rarely 933 Research & Development
## 560 560 26 No Travel_Rarely 933 Sales
## 561 561 36 No Travel_Frequently 1480 Research & Development
## 562 562 58 No Travel_Frequently 1216 Research & Development
## 563 563 27 No Travel_Rarely 1134 Research & Development
## 564 564 31 No Non-Travel 697 Research & Development
## 565 565 30 No Travel_Rarely 921 Research & Development
## 566 566 44 No Travel_Rarely 1037 Research & Development
## 567 567 32 No Travel_Rarely 128 Research & Development
## 568 568 30 Yes Travel_Rarely 740 Sales
## 569 569 42 No Travel_Rarely 462 Sales
## 570 570 50 No Travel_Rarely 939 Research & Development
## 571 571 41 No Travel_Rarely 337 Sales
## 572 572 21 Yes Travel_Rarely 1427 Research & Development
## 573 573 28 No Travel_Frequently 783 Sales
## 574 574 34 No Travel_Rarely 810 Sales
## 575 575 26 No Travel_Rarely 703 Sales
## 576 576 37 No Travel_Rarely 1115 Research & Development
## 577 577 38 No Travel_Rarely 1404 Sales
## 578 578 44 No Non-Travel 981 Research & Development
## 579 579 39 No Travel_Frequently 711 Research & Development
## 580 580 29 No Travel_Rarely 1329 Research & Development
## 581 581 25 No Travel_Rarely 882 Research & Development
## 582 582 42 No Travel_Rarely 810 Research & Development
## 583 583 38 No Travel_Rarely 833 Research & Development
## 584 584 28 No Travel_Rarely 760 Sales
## 585 585 44 No Travel_Rarely 1459 Research & Development
## 586 586 40 No Travel_Rarely 905 Research & Development
## 587 587 38 No Non-Travel 573 Research & Development
## 588 588 36 Yes Travel_Rarely 1218 Sales
## 589 589 37 No Travel_Rarely 1462 Research & Development
## 590 590 35 No Travel_Rarely 1142 Research & Development
## 591 591 22 No Non-Travel 457 Research & Development
## 592 592 34 Yes Travel_Rarely 790 Sales
## 593 593 28 Yes Travel_Frequently 1496 Sales
## 594 594 49 No Travel_Rarely 722 Research & Development
## 595 595 40 No Travel_Rarely 884 Research & Development
## 596 596 33 No Travel_Rarely 586 Sales
## 597 597 35 No Travel_Rarely 672 Research & Development
## 598 598 40 No Travel_Rarely 555 Research & Development
## 599 599 27 No Travel_Rarely 155 Research & Development
## 600 600 24 Yes Travel_Rarely 813 Research & Development
## 601 601 24 No Non-Travel 830 Sales
## 602 602 32 No Non-Travel 1200 Research & Development
## 603 603 30 No Travel_Rarely 153 Research & Development
## 604 604 29 No Travel_Rarely 1401 Research & Development
## 605 605 29 Yes Travel_Rarely 428 Sales
## 606 606 53 No Travel_Rarely 970 Research & Development
## 607 607 59 No Travel_Frequently 1225 Sales
## 608 608 38 No Travel_Rarely 322 Sales
## 609 609 26 No Travel_Rarely 652 Research & Development
## 610 610 33 No Non-Travel 1038 Sales
## 611 611 35 No Travel_Rarely 817 Research & Development
## 612 612 41 No Non-Travel 509 Research & Development
## 613 613 33 Yes Travel_Rarely 465 Research & Development
## 614 614 19 Yes Travel_Rarely 419 Sales
## 615 615 30 No Travel_Rarely 231 Sales
## 616 616 34 No Travel_Rarely 1157 Research & Development
## 617 617 33 No Travel_Rarely 1099 Research & Development
## 618 618 44 No Travel_Rarely 1488 Sales
## 619 619 39 No Travel_Rarely 613 Research & Development
## 620 620 35 No Non-Travel 727 Research & Development
## 621 621 20 Yes Travel_Frequently 871 Research & Development
## 622 622 47 No Travel_Frequently 1379 Research & Development
## 623 623 34 No Travel_Rarely 182 Research & Development
## 624 624 35 No Travel_Rarely 1146 Human Resources
## 625 625 28 Yes Travel_Frequently 289 Research & Development
## 626 626 27 No Travel_Rarely 1167 Research & Development
## 627 627 24 No Travel_Rarely 1476 Sales
## 628 628 38 No Travel_Rarely 343 Research & Development
## 629 629 55 No Travel_Frequently 1091 Research & Development
## 630 630 34 No Travel_Rarely 1442 Research & Development
## 631 631 55 No Travel_Rarely 836 Research & Development
## 632 632 34 No Travel_Rarely 937 Sales
## 633 633 38 No Travel_Rarely 1035 Sales
## 634 634 36 No Travel_Rarely 922 Research & Development
## 635 635 43 No Travel_Rarely 1001 Research & Development
## 636 636 35 No Travel_Frequently 636 Research & Development
## 637 637 42 No Travel_Rarely 469 Research & Development
## 638 638 34 No Travel_Rarely 1239 Sales
## 639 639 24 No Travel_Rarely 691 Research & Development
## 640 640 27 Yes Travel_Rarely 1420 Sales
## 641 641 30 No Travel_Rarely 1358 Research & Development
## 642 642 40 No Travel_Rarely 750 Research & Development
## 643 643 55 No Non-Travel 444 Research & Development
## 644 644 34 Yes Non-Travel 967 Research & Development
## 645 645 41 Yes Non-Travel 906 Research & Development
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## 647 647 18 Yes Non-Travel 247 Research & Development
## 648 648 26 No Travel_Rarely 390 Research & Development
## 649 649 35 No Travel_Rarely 776 Sales
## 650 650 40 Yes Travel_Rarely 1329 Research & Development
## 651 651 35 No Travel_Rarely 992 Research & Development
## 652 652 60 No Travel_Rarely 696 Sales
## 653 653 40 No Travel_Frequently 593 Research & Development
## 654 654 48 No Travel_Rarely 530 Sales
## 655 655 22 Yes Travel_Frequently 1368 Research & Development
## 656 656 26 No Travel_Frequently 496 Research & Development
## 657 657 51 No Travel_Rarely 1469 Research & Development
## 658 658 54 No Travel_Frequently 1050 Research & Development
## 659 659 41 No Travel_Rarely 483 Research & Development
## 660 660 36 No Travel_Frequently 1195 Research & Development
## 661 661 32 No Travel_Frequently 430 Research & Development
## 662 662 34 No Travel_Rarely 1303 Research & Development
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## 664 664 35 No Travel_Rarely 802 Research & Development
## 665 665 34 No Non-Travel 1065 Sales
## 666 666 29 No Travel_Frequently 461 Research & Development
## 667 667 29 No Travel_Frequently 1413 Sales
## 668 668 33 Yes Travel_Rarely 587 Research & Development
## 669 669 32 Yes Travel_Rarely 1089 Research & Development
## 670 670 54 No Travel_Rarely 685 Research & Development
## 671 671 37 No Travel_Rarely 397 Research & Development
## 672 672 33 No Travel_Rarely 392 Sales
## 673 673 28 No Travel_Frequently 467 Sales
## 674 674 35 Yes Travel_Frequently 130 Research & Development
## 675 675 27 No Travel_Rarely 486 Research & Development
## 676 676 46 No Travel_Rarely 168 Sales
## 677 677 36 No Travel_Rarely 676 Research & Development
## 678 678 40 No Travel_Frequently 692 Research & Development
## 679 679 36 No Travel_Frequently 688 Research & Development
## 680 680 31 No Travel_Rarely 154 Sales
## 681 681 25 No Travel_Rarely 583 Sales
## 682 682 43 No Travel_Frequently 313 Research & Development
## 683 683 36 No Travel_Rarely 1351 Research & Development
## 684 684 57 No Travel_Rarely 334 Research & Development
## 685 685 28 No Travel_Rarely 440 Research & Development
## 686 686 31 No Travel_Rarely 1274 Research & Development
## 687 687 29 Yes Travel_Frequently 337 Research & Development
## 688 688 33 No Travel_Rarely 147 Research & Development
## 689 689 19 Yes Travel_Rarely 303 Research & Development
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## 693 693 42 No Travel_Rarely 1332 Research & Development
## 694 694 30 Yes Travel_Frequently 464 Research & Development
## 695 695 37 No Travel_Frequently 1278 Sales
## 696 696 47 No Travel_Rarely 1454 Sales
## 697 697 48 No Travel_Frequently 365 Research & Development
## 698 698 36 No Travel_Rarely 1396 Research & Development
## 699 699 18 Yes Travel_Frequently 544 Sales
## 700 700 46 No Travel_Rarely 1450 Research & Development
## 701 701 29 No Travel_Rarely 352 Human Resources
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## 709 709 38 No Travel_Rarely 1153 Research & Development
## 710 710 50 No Non-Travel 881 Research & Development
## 711 711 30 No Travel_Rarely 634 Research & Development
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## 713 713 48 No Travel_Rarely 969 Research & Development
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## 715 715 36 No Non-Travel 1434 Sales
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## 718 718 28 No Travel_Rarely 857 Research & Development
## 719 719 35 Yes Travel_Frequently 880 Sales
## 720 720 28 No Travel_Rarely 950 Research & Development
## 721 721 37 No Travel_Rarely 482 Research & Development
## 722 722 37 No Travel_Rarely 671 Research & Development
## 723 723 34 No Travel_Frequently 560 Research & Development
## 724 724 36 No Travel_Rarely 796 Research & Development
## 725 725 42 No Travel_Rarely 1142 Research & Development
## 726 726 25 No Travel_Rarely 685 Research & Development
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## 728 728 34 No Travel_Frequently 878 Research & Development
## 729 729 26 Yes Travel_Rarely 471 Research & Development
## 730 730 22 No Travel_Rarely 594 Research & Development
## 731 731 43 No Travel_Rarely 177 Research & Development
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## 733 733 27 Yes Travel_Frequently 1337 Human Resources
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## 737 737 39 No Travel_Rarely 722 Sales
## 738 738 59 No Travel_Rarely 326 Sales
## 739 739 35 No Travel_Rarely 1219 Sales
## 740 740 35 No Travel_Rarely 538 Research & Development
## 741 741 25 No Travel_Rarely 959 Sales
## 742 742 31 No Travel_Rarely 688 Sales
## 743 743 37 No Travel_Rarely 674 Research & Development
## 744 744 23 No Travel_Rarely 160 Research & Development
## 745 745 25 No Travel_Rarely 1382 Sales
## 746 746 24 No Travel_Rarely 350 Research & Development
## 747 747 29 Yes Travel_Rarely 350 Human Resources
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## 749 749 24 No Travel_Rarely 506 Research & Development
## 750 750 33 No Travel_Rarely 134 Research & Development
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## 752 752 31 Yes Travel_Frequently 1060 Sales
## 753 753 50 No Travel_Frequently 1234 Research & Development
## 754 754 46 Yes Travel_Rarely 1254 Sales
## 755 755 54 No Travel_Rarely 1217 Research & Development
## 756 756 44 No Non-Travel 381 Research & Development
## 757 757 35 No Travel_Rarely 1137 Research & Development
## 758 758 49 No Travel_Rarely 1495 Research & Development
## 759 759 38 No Travel_Rarely 1495 Research & Development
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## 761 761 29 Yes Travel_Rarely 906 Research & Development
## 762 762 47 No Travel_Rarely 202 Research & Development
## 763 763 41 No Non-Travel 256 Sales
## 764 764 50 No Non-Travel 145 Sales
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## 766 766 45 No Travel_Rarely 1015 Research & Development
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## 768 768 40 No Non-Travel 1094 Sales
## 769 769 46 No Travel_Rarely 150 Research & Development
## 770 770 45 No Non-Travel 1050 Sales
## 771 771 33 No Travel_Frequently 508 Sales
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## 773 773 39 No Travel_Frequently 505 Research & Development
## 774 774 36 Yes Travel_Rarely 318 Research & Development
## 775 775 42 No Travel_Frequently 288 Research & Development
## 776 776 23 No Travel_Rarely 310 Research & Development
## 777 777 30 No Travel_Rarely 330 Human Resources
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## 779 779 26 No Non-Travel 786 Research & Development
## 780 780 29 Yes Travel_Rarely 1092 Research & Development
## 781 781 41 Yes Travel_Rarely 1356 Sales
## 782 782 39 No Travel_Rarely 408 Research & Development
## 783 783 33 No Travel_Rarely 1075 Human Resources
## 784 784 33 No Travel_Frequently 1146 Sales
## 785 785 47 No Non-Travel 543 Sales
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## 787 787 51 No Travel_Rarely 942 Research & Development
## 788 788 52 Yes Travel_Rarely 723 Research & Development
## 789 789 55 No Travel_Rarely 478 Research & Development
## 790 790 25 No Travel_Rarely 266 Research & Development
## 791 791 49 No Travel_Rarely 271 Research & Development
## 792 792 34 No Travel_Rarely 629 Research & Development
## 793 793 38 No Travel_Rarely 397 Research & Development
## 794 794 36 No Travel_Rarely 1223 Research & Development
## 795 795 49 No Non-Travel 1002 Research & Development
## 796 796 33 Yes Travel_Frequently 1076 Research & Development
## 797 797 45 No Travel_Rarely 1385 Research & Development
## 798 798 22 Yes Travel_Rarely 1294 Research & Development
## 799 799 19 Yes Non-Travel 504 Research & Development
## 800 800 36 No Travel_Rarely 1040 Research & Development
## 801 801 33 No Travel_Frequently 1141 Sales
## 802 802 31 No Travel_Rarely 828 Sales
## 803 803 39 No Travel_Rarely 117 Research & Development
## 804 804 35 Yes Travel_Rarely 1204 Sales
## 805 805 44 No Non-Travel 489 Research & Development
## 806 806 36 No Travel_Rarely 1383 Research & Development
## 807 807 25 No Travel_Rarely 1372 Sales
## 808 808 22 No Travel_Rarely 1256 Research & Development
## 809 809 30 No Travel_Rarely 793 Research & Development
## 810 810 27 No Travel_Rarely 1157 Research & Development
## 811 811 30 No Non-Travel 111 Research & Development
## 812 812 25 No Travel_Rarely 949 Research & Development
## 813 813 24 No Travel_Rarely 1371 Sales
## 814 814 47 No Travel_Rarely 1176 Human Resources
## 815 815 38 No Travel_Rarely 395 Sales
## 816 816 33 No Travel_Rarely 117 Research & Development
## 817 817 30 No Travel_Rarely 946 Research & Development
## 818 818 33 No Travel_Rarely 213 Research & Development
## 819 819 30 No Travel_Rarely 1427 Research & Development
## 820 820 28 Yes Travel_Rarely 1475 Sales
## 821 821 32 No Travel_Rarely 634 Research & Development
## 822 822 29 No Travel_Rarely 1247 Sales
## 823 823 34 No Travel_Rarely 704 Sales
## 824 824 31 Yes Travel_Frequently 874 Research & Development
## 825 825 36 No Travel_Rarely 559 Research & Development
## 826 826 46 No Travel_Rarely 430 Research & Development
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## 828 828 34 No Travel_Rarely 131 Sales
## 829 829 38 No Travel_Frequently 594 Research & Development
## 830 830 50 No Travel_Frequently 1246 Human Resources
## 831 831 45 No Non-Travel 248 Research & Development
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## 833 833 28 No Travel_Rarely 1179 Research & Development
## 834 834 19 No Travel_Rarely 645 Research & Development
## 835 835 55 No Non-Travel 177 Research & Development
## 836 836 37 No Non-Travel 142 Sales
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## 838 838 34 No Travel_Rarely 258 Sales
## 839 839 47 No Travel_Rarely 1001 Research & Development
## 840 840 48 No Travel_Rarely 1224 Research & Development
## 841 841 18 Yes Travel_Rarely 230 Research & Development
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## 843 843 52 No Travel_Frequently 890 Research & Development
## 844 844 34 No Travel_Rarely 943 Research & Development
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## 846 846 28 No Travel_Rarely 1300 Research & Development
## 847 847 19 No Travel_Rarely 1181 Research & Development
## 848 848 39 No Travel_Rarely 1132 Research & Development
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## 852 852 30 No Travel_Rarely 125 Research & Development
## 853 853 29 No Travel_Rarely 153 Research & Development
## 854 854 28 No Travel_Rarely 1476 Research & Development
## 855 855 35 No Travel_Rarely 219 Research & Development
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## 857 857 30 Yes Travel_Rarely 1005 Research & Development
## 858 858 31 No Travel_Frequently 798 Research & Development
## 859 859 41 No Travel_Rarely 933 Research & Development
## 860 860 44 Yes Travel_Rarely 621 Research & Development
## 861 861 51 No Travel_Frequently 968 Research & Development
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## 864 864 47 No Travel_Frequently 217 Sales
## 865 865 45 No Travel_Rarely 1448 Research & Development
## 866 866 48 No Travel_Rarely 855 Research & Development
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## 937 1237 53 <NA> Travel_Rarely 102 Research & Development
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## 941 1241 35 <NA> Travel_Rarely 755 Research & Development
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## 943 1243 54 <NA> Travel_Rarely 376 Research & Development
## 944 1244 37 <NA> Travel_Rarely 783 Research & Development
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## 946 1246 28 <NA> Travel_Rarely 640 Research & Development
## 947 1247 57 <NA> Travel_Rarely 593 Research & Development
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## 977 1277 36 <NA> Travel_Frequently 1302 Research & Development
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## 984 1284 38 <NA> Travel_Rarely 1084 Research & Development
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## 987 1287 30 <NA> Travel_Rarely 1465 Research & Development
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## 989 1289 24 <NA> Non-Travel 1092 Research & Development
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## 991 1291 38 <NA> Travel_Rarely 1321 Sales
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## 998 1298 50 <NA> Travel_Rarely 1099 Research & Development
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## 1003 1303 49 <NA> Travel_Rarely 1091 Research & Development
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## 1006 1306 35 <NA> Travel_Rarely 1315 Research & Development
## 1007 1307 36 <NA> Travel_Rarely 1425 Research & Development
## 1008 1308 42 <NA> Travel_Rarely 1210 Research & Development
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## 1014 1314 32 <NA> Travel_Rarely 120 Research & Development
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## 1017 1317 42 <NA> Travel_Rarely 188 Research & Development
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## 1021 1321 33 <NA> Travel_Rarely 832 Research & Development
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## 1023 1323 40 <NA> Travel_Frequently 530 Research & Development
## 1024 1324 30 <NA> Travel_Rarely 1092 Research & Development
## 1025 1325 40 <NA> Travel_Rarely 804 Research & Development
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## 1029 1329 21 <NA> Travel_Rarely 391 Research & Development
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## 1032 1332 28 <NA> Travel_Rarely 1083 Research & Development
## 1033 1333 34 <NA> Travel_Rarely 699 Research & Development
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## 1048 1348 35 <NA> Travel_Rarely 1349 Research & Development
## 1049 1349 44 <NA> Travel_Rarely 1315 Research & Development
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## 1055 1355 43 <NA> Travel_Rarely 982 Research & Development
## 1056 1356 53 <NA> Travel_Rarely 238 Sales
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## 1058 1358 20 <NA> Travel_Rarely 805 Research & Development
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## 1179 879 30 No Travel_Frequently 1312 Research & Development
## 1180 880 51 No Travel_Rarely 1405 Research & Development
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## 1187 887 29 No Travel_Rarely 726 Research & Development
## 1188 888 34 No Travel_Rarely 167 Research & Development
## 1189 889 31 No Travel_Rarely 655 Research & Development
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## 1201 901 27 No Travel_Rarely 591 Research & Development
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## 1205 905 45 No Travel_Rarely 1234 Sales
## 1206 906 33 No Travel_Frequently 970 Sales
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## 1209 909 28 Yes Travel_Rarely 1434 Research & Development
## 1210 910 32 No Travel_Rarely 827 Research & Development
## 1211 911 43 No Travel_Rarely 920 Research & Development
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## 1216 916 23 No Travel_Rarely 507 Research & Development
## 1217 917 28 No Travel_Rarely 1217 Research & Development
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## 1220 920 59 No Travel_Rarely 1324 Research & Development
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## 1222 922 35 No Travel_Rarely 1229 Research & Development
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## 1236 936 24 Yes Travel_Frequently 381 Research & Development
## 1237 937 46 No Travel_Rarely 644 Research & Development
## 1238 938 51 Yes Travel_Frequently 1150 Research & Development
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## 1243 943 59 No Travel_Rarely 1435 Sales
## 1244 944 27 No Travel_Rarely 1055 Research & Development
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## 1263 963 38 No Travel_Frequently 471 Research & Development
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## 1266 966 50 No Travel_Rarely 989 Research & Development
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## 1275 975 24 No Travel_Rarely 1206 Research & Development
## 1276 976 29 Yes Travel_Rarely 896 Research & Development
## 1277 977 48 No Travel_Rarely 277 Research & Development
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## 1280 980 49 No Travel_Rarely 1098 Research & Development
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## 1283 983 52 No Travel_Rarely 1323 Research & Development
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## 1287 987 22 Yes Travel_Frequently 1256 Research & Development
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## 1309 1009 53 No Travel_Rarely 1223 Research & Development
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## 1317 1017 42 No Travel_Rarely 269 Research & Development
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## 1319 1019 30 No Travel_Rarely 1339 Sales
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## 1352 1052 36 No Travel_Frequently 635 Research & Development
## 1353 1053 43 No Travel_Rarely 1473 Research & Development
## 1354 1054 28 No Travel_Rarely 995 Research & Development
## 1355 1055 27 No Travel_Frequently 591 Research & Development
## 1356 1056 25 Yes Travel_Rarely 688 Research & Development
## 1357 1057 27 No Travel_Frequently 472 Research & Development
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## 1367 1067 40 No Travel_Rarely 1194 Research & Development
## 1368 1068 60 No Travel_Rarely 422 Research & Development
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## 1374 1074 30 No Travel_Rarely 305 Research & Development
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## 1377 1077 53 No Travel_Rarely 1282 Research & Development
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## 1388 1088 38 No Travel_Rarely 268 Research & Development
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## 1416 1116 27 No Travel_Frequently 1131 Research & Development
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## 1433 1133 42 No Travel_Rarely 622 Research & Development
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## 1435 1135 32 Yes Travel_Rarely 1033 Research & Development
## 1436 1136 26 No Travel_Rarely 192 Research & Development
## 1437 1137 37 No Travel_Rarely 921 Research & Development
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## 1443 1143 48 No Travel_Rarely 1355 Research & Development
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## 1449 1149 29 No Travel_Rarely 1010 Research & Development
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## 1453 1153 54 No Travel_Frequently 928 Research & Development
## 1454 1154 28 Yes Travel_Rarely 1485 Research & Development
## 1455 1155 49 No Travel_Rarely 470 Research & Development
## 1456 1156 51 No Travel_Rarely 313 Research & Development
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## 1458 1158 36 No Travel_Rarely 430 Research & Development
## 1459 1159 27 No Travel_Rarely 199 Research & Development
## 1460 1160 36 No Travel_Rarely 216 Research & Development
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## 1462 1162 38 No Travel_Rarely 168 Research & Development
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## 1464 1164 37 No Travel_Rarely 799 Research & Development
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## 1466 1166 32 No Travel_Frequently 116 Research & Development
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## 1468 1168 30 No Travel_Rarely 1082 Sales
## 1469 1169 27 No Travel_Rarely 798 Research & Development
## 1470 1170 37 No Travel_Rarely 290 Research & Development
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## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## 1 Divorced 4403 9250 2 Y
## 2 Single 19626 17544 1 Y
## 3 Single 9362 19944 2 Y
## 4 Married 10422 24032 1 Y
## 5 Single 3760 17218 1 Y
## 6 Divorced 8793 4809 1 Y
## 7 Married 2127 5561 2 Y
## 8 Divorced 6694 24223 2 Y
## 9 Married 2220 18410 1 Y
## 10 Married 5063 15332 1 Y
## 11 Married 19392 22539 7 Y
## 12 Divorced 2706 10494 1 Y
## 13 Single 10221 18869 3 Y
## 14 Married 2476 17434 1 Y
## 15 Single 3423 22957 6 Y
## 16 Married 6932 24406 1 Y
## 17 Married 6725 13554 1 Y
## 18 Married 5258 16044 2 Y
## 19 Married 2932 5586 0 Y
## 20 Married 5033 9364 2 Y
## 21 Divorced 8847 13934 2 Y
## 22 Married 8120 18597 3 Y
## 23 Divorced 5679 19627 3 Y
## 24 Single 5332 21602 7 Y
## 25 Single 6949 12291 0 Y
## 26 Married 18213 8751 7 Y
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## 28 Single 8224 18385 0 Y
## 29 Single 10448 5843 6 Y
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## 1357 Married NA 9679 5 Y
## 1358 Married NA 7744 5 Y
## 1359 Divorced NA 12832 2 Y
## 1360 Married NA 19911 0 Y
## 1361 Married NA 2721 0 Y
## 1362 Divorced NA 17000 1 Y
## 1363 Divorced NA 5335 3 Y
## 1364 Divorced NA 25103 1 Y
## 1365 Divorced NA 17056 2 Y
## 1366 Married NA 4344 1 Y
## 1367 Married NA 12549 2 Y
## 1368 Married NA 3854 5 Y
## 1369 Married NA 17456 0 Y
## 1370 Married NA 9983 7 Y
## 1371 Married NA 6527 8 Y
## 1372 Married NA 4156 1 Y
## 1373 Single NA 5050 1 Y
## 1374 Married NA 9128 3 Y
## 1375 Married NA 20328 4 Y
## 1376 Married NA 24097 8 Y
## 1377 Divorced NA 10735 4 Y
## 1378 Divorced NA 18624 1 Y
## 1379 Single NA 26285 4 Y
## 1380 Divorced NA 12932 2 Y
## 1381 Single NA 2725 2 Y
## 1382 Married NA 8952 1 Y
## 1383 Married NA 26956 1 Y
## 1384 Married NA 18659 9 Y
## 1385 Single NA 8787 0 Y
## 1386 Married NA 23772 5 Y
## 1387 Married NA 20943 1 Y
## 1388 Married NA 20471 6 Y
## 1389 Married NA 10077 0 Y
## 1390 Married NA 7100 1 Y
## 1391 Divorced NA 11866 1 Y
## 1392 Divorced NA 10436 1 Y
## 1393 Divorced NA 10056 1 Y
## 1394 Divorced NA 11591 7 Y
## 1395 Divorced NA 18384 1 Y
## 1396 Married NA 15701 6 Y
## 1397 Married NA 18697 2 Y
## 1398 Divorced NA 9732 6 Y
## 1399 Married NA 3956 1 Y
## 1400 Single NA 4992 1 Y
## 1401 Married NA 6499 1 Y
## 1402 Divorced NA 22553 7 Y
## 1403 Married NA 19682 3 Y
## 1404 Married NA 20715 7 Y
## 1405 Single NA 23293 2 Y
## 1406 Divorced NA 17940 0 Y
## 1407 Married NA 14377 1 Y
## 1408 Single NA 26894 4 Y
## 1409 Single NA 12916 1 Y
## 1410 Married NA 15975 0 Y
## 1411 Married NA 12250 1 Y
## 1412 Married NA 11038 3 Y
## 1413 Married NA 24619 7 Y
## 1414 Married NA 23452 3 Y
## 1415 Single NA 13084 2 Y
## 1416 Married NA 23844 0 Y
## 1417 Single NA 21123 2 Y
## 1418 Married NA 16392 0 Y
## 1419 Divorced NA 9255 2 Y
## 1420 Divorced NA 2912 1 Y
## 1421 Divorced NA 16586 4 Y
## 1422 Single NA 16154 2 Y
## 1423 Married NA 5771 1 Y
## 1424 Married NA 20462 9 Y
## 1425 Married NA 21643 0 Y
## 1426 Single NA 20989 0 Y
## 1427 Married NA 14168 1 Y
## 1428 Married NA 21821 8 Y
## 1429 Single NA 15896 1 Y
## 1430 Married NA 6881 1 Y
## 1431 Divorced NA 13523 9 Y
## 1432 Divorced NA 17334 6 Y
## 1433 Married NA 5718 1 Y
## 1434 Single NA 23826 1 Y
## 1435 Single NA 10224 7 Y
## 1436 Married NA 11141 1 Y
## 1437 Married NA 17663 6 Y
## 1438 Divorced NA 4297 0 Y
## 1439 Divorced NA 9647 0 Y
## 1440 Married NA 3525 1 Y
## 1441 Married NA 14408 0 Y
## 1442 Married NA 21173 0 Y
## 1443 Single NA 22245 7 Y
## 1444 Single NA 24252 1 Y
## 1445 Married NA 10503 4 Y
## 1446 Single NA 23333 8 Y
## 1447 Married NA 10228 2 Y
## 1448 Divorced NA 16031 8 Y
## 1449 Divorced NA 5598 1 Y
## 1450 Married NA 15830 1 Y
## 1451 Divorced NA 15748 1 Y
## 1452 Single NA 20392 3 Y
## 1453 Single NA 16885 3 Y
## 1454 Married NA 22955 1 Y
## 1455 Married NA 5549 1 Y
## 1456 Single NA 7192 3 Y
## 1457 Divorced NA 23522 6 Y
## 1458 Married NA 19573 4 Y
## 1459 Married NA 7950 1 Y
## 1460 Divorced NA 2819 6 Y
## 1461 Single NA 10826 1 Y
## 1462 Single NA 15397 4 Y
## 1463 Married NA 7003 1 Y
## 1464 Single NA 23848 4 Y
## 1465 Married NA 20898 1 Y
## 1466 Married NA 7331 1 Y
## 1467 Married NA 26542 4 Y
## 1468 Single NA 19558 0 Y
## 1469 Divorced NA 5013 0 Y
## 1470 Married NA 22977 1 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 No 11 3 3
## 2 No 14 3 1
## 3 No 11 3 3
## 4 No 19 3 3
## 5 Yes 13 3 3
## 6 No 21 4 3
## 7 Yes 12 3 1
## 8 Yes 14 3 3
## 9 Yes 19 3 4
## 10 No 14 3 2
## 11 No 13 3 4
## 12 No 15 3 2
## 13 No 21 4 2
## 14 No 18 3 1
## 15 No 12 3 3
## 16 No 13 3 4
## 17 No 12 3 3
## 18 No 14 3 3
## 19 Yes 14 3 1
## 20 No 15 3 4
## 21 Yes 11 3 3
## 22 No 12 3 4
## 23 Yes 13 3 2
## 24 No 13 3 4
## 25 No 14 3 1
## 26 No 11 3 3
## 27 No 11 3 4
## 28 Yes 17 3 1
## 29 Yes 13 3 2
## 30 No 18 3 1
## 31 No 17 3 4
## 32 No 18 3 4
## 33 Yes 12 3 2
## 34 No 12 3 2
## 35 No 24 4 2
## 36 No 20 4 3
## 37 No 17 3 3
## 38 Yes 13 3 2
## 39 No 14 3 1
## 40 No 12 3 1
## 41 No 22 4 4
## 42 No 13 3 1
## 43 Yes 16 3 2
## 44 No 22 4 4
## 45 No 22 4 4
## 46 No 13 3 3
## 47 No 15 3 2
## 48 Yes 14 3 2
## 49 Yes 14 3 4
## 50 No 18 3 4
## 51 No 17 3 4
## 52 Yes 21 4 3
## 53 No 13 3 4
## 54 No 12 3 2
## 55 No 23 4 4
## 56 No 20 4 2
## 57 Yes 12 3 4
## 58 Yes 18 3 4
## 59 Yes 18 3 1
## 60 Yes 14 3 4
## 61 Yes 11 3 1
## 62 No 12 3 1
## 63 No 19 3 3
## 64 No 16 3 3
## 65 No 13 3 1
## 66 Yes 12 3 4
## 67 No 13 3 2
## 68 No 13 3 3
## 69 No 13 3 4
## 70 No 12 3 2
## 71 No 22 4 1
## 72 No 18 3 4
## 73 Yes 13 3 1
## 74 Yes 11 3 1
## 75 Yes 19 3 3
## 76 No 23 4 4
## 77 Yes 14 3 4
## 78 No 15 3 2
## 79 No 12 3 4
## 80 Yes 11 3 3
## 81 No 15 3 1
## 82 No 16 3 2
## 83 No 11 3 4
## 84 Yes 13 3 1
## 85 Yes 11 3 4
## 86 Yes 13 3 1
## 87 No 12 3 4
## 88 Yes 24 4 4
## 89 No 15 3 4
## 90 No 12 3 4
## 91 Yes 23 4 3
## 92 No 13 3 1
## 93 Yes 15 3 2
## 94 Yes 16 3 3
## 95 No 15 3 2
## 96 No 25 4 3
## 97 No 12 3 4
## 98 No 14 3 4
## 99 No 13 3 3
## 100 No 17 3 1
## 101 No 14 3 2
## 102 No 18 3 1
## 103 No 15 3 1
## 104 No 16 3 1
## 105 No 12 3 2
## 106 No 22 4 2
## 107 No 13 3 1
## 108 Yes 18 3 1
## 109 No 22 4 4
## 110 No 14 3 4
## 111 Yes 14 3 4
## 112 No 15 3 2
## 113 No 19 3 4
## 114 No 11 3 1
## 115 Yes 15 3 4
## 116 Yes 13 3 4
## 117 No 14 3 2
## 118 Yes 13 3 1
## 119 No 12 3 1
## 120 No 11 3 4
## 121 No 14 3 1
## 122 Yes 11 3 4
## 123 Yes 12 3 3
## 124 No 20 4 1
## 125 No 18 3 1
## 126 Yes 11 3 4
## 127 Yes 22 4 2
## 128 No 13 3 4
## 129 Yes 14 3 1
## 130 No 12 3 4
## 131 Yes 13 3 2
## 132 No 12 3 3
## 133 No 11 3 4
## 134 No 21 4 3
## 135 No 11 3 2
## 136 No 24 4 4
## 137 Yes 11 3 4
## 138 No 11 3 3
## 139 Yes 21 4 4
## 140 No 12 3 1
## 141 Yes 12 3 1
## 142 No 12 3 4
## 143 No 13 3 2
## 144 No 14 3 2
## 145 No 20 4 2
## 146 No 14 3 1
## 147 No 13 3 4
## 148 No 13 3 4
## 149 No 22 4 4
## 150 No 21 4 2
## 151 No 17 3 3
## 152 No 12 3 4
## 153 No 14 3 3
## 154 No 12 3 1
## 155 No 14 3 2
## 156 Yes 12 3 2
## 157 No 14 3 3
## 158 No 11 3 4
## 159 No 11 3 3
## 160 Yes 17 3 3
## 161 Yes 12 3 3
## 162 No 12 3 1
## 163 No 12 3 4
## 164 Yes 13 3 3
## 165 No 15 3 3
## 166 No 13 3 4
## 167 No 16 3 2
## 168 No 19 3 3
## 169 Yes 12 3 2
## 170 No 22 4 1
## 171 No 23 4 4
## 172 No 18 3 3
## 173 No 11 3 4
## 174 Yes 14 3 4
## 175 No 13 3 3
## 176 Yes 11 3 3
## 177 Yes 13 3 4
## 178 Yes 14 3 2
## 179 No 14 3 4
## 180 No 22 4 3
## 181 No 18 3 2
## 182 No 15 3 4
## 183 No 12 3 4
## 184 No 18 3 4
## 185 No 11 3 1
## 186 No 23 4 4
## 187 No 17 3 3
## 188 No 14 3 3
## 189 No 14 3 1
## 190 No 15 3 2
## 191 No 13 3 4
## 192 No 14 3 4
## 193 No 14 3 3
## 194 No 14 3 1
## 195 No 12 3 4
## 196 No 11 3 2
## 197 No 23 4 2
## 198 No 17 3 2
## 199 No 14 3 4
## 200 No 21 4 4
## 201 Yes 16 3 4
## 202 No 17 3 2
## 203 No 12 3 3
## 204 Yes 13 3 4
## 205 No 11 3 1
## 206 Yes 13 3 4
## 207 No 13 3 2
## 208 Yes 19 3 2
## 209 No 14 3 4
## 210 Yes 13 3 3
## 211 No 23 4 3
## 212 No 13 3 2
## 213 No 17 3 1
## 214 Yes 13 3 4
## 215 No 13 3 4
## 216 No 14 3 3
## 217 No 19 3 3
## 218 No 11 3 2
## 219 No 14 3 3
## 220 Yes 20 4 2
## 221 No 22 4 3
## 222 No 14 3 4
## 223 Yes 25 4 3
## 224 No 14 3 4
## 225 No 19 3 4
## 226 No 13 3 3
## 227 No 12 3 3
## 228 Yes 16 3 1
## 229 No 21 4 1
## 230 Yes 19 3 2
## 231 No 19 3 1
## 232 No 13 3 1
## 233 No 11 3 3
## 234 No 14 3 4
## 235 Yes 12 3 4
## 236 No 11 3 3
## 237 No 11 3 1
## 238 Yes 12 3 4
## 239 No 14 3 3
## 240 No 15 3 3
## 241 Yes 21 4 4
## 242 No 22 4 3
## 243 No 23 4 4
## 244 No 12 3 1
## 245 Yes 15 3 3
## 246 No 14 3 3
## 247 Yes 14 3 3
## 248 No 14 3 2
## 249 Yes 14 3 1
## 250 No 16 3 2
## 251 No 12 3 3
## 252 No 13 3 4
## 253 No 14 3 4
## 254 No 17 3 3
## 255 No 11 3 3
## 256 No 13 3 3
## 257 No 11 3 3
## 258 No 14 3 2
## 259 Yes 13 3 4
## 260 Yes 19 3 3
## 261 No 14 3 3
## 262 No 13 3 3
## 263 Yes 13 3 3
## 264 No 15 3 2
## 265 No 12 3 3
## 266 No 13 3 3
## 267 No 14 3 4
## 268 No 12 3 1
## 269 No 20 4 3
## 270 No 15 3 3
## 271 No 22 4 2
## 272 No 12 3 3
## 273 No 21 4 4
## 274 No 13 3 1
## 275 No 11 3 4
## 276 No 13 3 4
## 277 No 13 3 4
## 278 No 19 3 1
## 279 Yes 11 3 4
## 280 Yes 16 3 2
## 281 Yes 17 3 1
## 282 Yes 13 3 3
## 283 No 17 3 4
## 284 No 12 3 4
## 285 No 13 3 4
## 286 Yes 12 3 2
## 287 Yes 23 4 1
## 288 No 11 3 3
## 289 No 14 3 3
## 290 No 16 3 2
## 291 No 14 3 4
## 292 No 15 3 3
## 293 No 16 3 4
## 294 No 18 3 1
## 295 Yes 21 4 4
## 296 Yes 11 3 3
## 297 No 17 3 3
## 298 Yes 24 4 4
## 299 Yes 22 4 1
## 300 Yes 13 3 4
## 301 No 14 3 4
## 302 No 22 4 4
## 303 No 12 3 4
## 304 No 19 3 2
## 305 No 24 4 1
## 306 No 17 3 4
## 307 No 18 3 2
## 308 No 17 3 4
## 309 Yes 14 3 4
## 310 No 22 4 3
## 311 No 17 3 2
## 312 No 15 3 3
## 313 No 12 3 1
## 314 No 17 3 3
## 315 No 14 3 4
## 316 Yes 17 3 3
## 317 No 18 3 4
## 318 No 12 3 2
## 319 Yes 12 3 4
## 320 Yes 12 3 4
## 321 No 20 4 3
## 322 No 12 3 4
## 323 No 12 3 2
## 324 Yes 23 4 1
## 325 No 16 3 3
## 326 No 19 3 3
## 327 Yes 14 3 4
## 328 Yes 17 3 3
## 329 Yes 16 3 3
## 330 No 14 3 3
## 331 No 14 3 1
## 332 No 11 3 3
## 333 Yes 12 3 3
## 334 No 11 3 4
## 335 No 21 4 4
## 336 No 22 4 2
## 337 No 11 3 4
## 338 No 17 3 3
## 339 Yes 14 3 4
## 340 No 13 3 4
## 341 Yes 14 3 4
## 342 Yes 12 3 2
## 343 No 15 3 3
## 344 No 20 4 3
## 345 No 19 3 3
## 346 No 12 3 1
## 347 No 11 3 4
## 348 No 20 4 1
## 349 No 13 3 4
## 350 No 11 3 3
## 351 No 13 3 2
## 352 No 11 3 2
## 353 Yes 13 3 4
## 354 Yes 18 3 3
## 355 Yes 13 3 4
## 356 No 18 3 4
## 357 No 19 3 3
## 358 No 16 3 2
## 359 Yes 22 4 2
## 360 No 11 3 3
## 361 No 14 3 1
## 362 No 17 3 4
## 363 No 15 3 4
## 364 No 19 3 4
## 365 No 14 3 4
## 366 No 11 3 4
## 367 No 13 3 3
## 368 No 12 3 4
## 369 No 16 3 4
## 370 No 16 3 3
## 371 No 12 3 3
## 372 No 11 3 3
## 373 No 18 3 1
## 374 No 11 3 1
## 375 No 11 3 1
## 376 Yes 13 3 4
## 377 No 16 3 3
## 378 Yes 12 3 3
## 379 Yes 18 3 1
## 380 Yes 11 3 2
## 381 No 11 3 2
## 382 No 23 4 4
## 383 No 11 3 1
## 384 No 13 3 3
## 385 Yes 12 3 3
## 386 No 13 3 2
## 387 No 14 3 1
## 388 No 12 3 2
## 389 No 15 3 3
## 390 Yes 11 3 3
## 391 No 17 3 2
## 392 Yes 19 3 3
## 393 Yes 13 3 4
## 394 No 19 3 4
## 395 Yes 14 3 4
## 396 Yes 18 3 2
## 397 No 13 3 3
## 398 No 15 3 4
## 399 No 21 4 1
## 400 Yes 18 3 2
## 401 No 20 4 3
## 402 No 16 3 2
## 403 No 12 3 3
## 404 No 12 3 3
## 405 No 11 3 4
## 406 Yes 14 3 3
## 407 No 14 3 1
## 408 No 14 3 3
## 409 No 16 3 3
## 410 No 24 4 3
## 411 No 21 4 3
## 412 No 12 3 2
## 413 No 21 4 1
## 414 No 13 3 3
## 415 No 16 3 4
## 416 No 14 3 3
## 417 No 14 3 3
## 418 No 18 3 4
## 419 No 13 3 2
## 420 No 18 3 3
## 421 No 15 3 3
## 422 Yes 15 3 1
## 423 Yes 18 3 2
## 424 No 15 3 3
## 425 No 11 3 3
## 426 No 13 3 3
## 427 No 13 3 2
## 428 No 17 3 1
## 429 Yes 14 3 4
## 430 No 13 3 2
## 431 Yes 11 3 1
## 432 No 16 3 2
## 433 No 20 4 1
## 434 No 13 3 3
## 435 No 12 3 2
## 436 No 18 3 1
## 437 No 17 3 4
## 438 No 13 3 4
## 439 Yes 14 3 3
## 440 No 13 3 3
## 441 Yes 14 3 4
## 442 No 17 3 3
## 443 No 11 3 3
## 444 Yes 18 3 1
## 445 Yes 17 3 3
## 446 No 12 3 3
## 447 No 15 3 4
## 448 No 18 3 3
## 449 No 11 3 4
## 450 No 15 3 1
## 451 Yes 13 3 1
## 452 No 13 3 3
## 453 Yes 18 3 4
## 454 No 14 3 2
## 455 No 11 3 1
## 456 No 14 3 2
## 457 No 11 3 1
## 458 Yes 22 4 4
## 459 No 22 4 4
## 460 No 16 3 3
## 461 No 17 3 4
## 462 No 11 3 3
## 463 No 11 3 4
## 464 No 20 4 4
## 465 Yes 16 3 4
## 466 Yes 11 3 3
## 467 Yes 21 4 4
## 468 No 12 3 2
## 469 No 18 3 4
## 470 No 11 3 3
## 471 No 11 3 3
## 472 Yes 24 4 2
## 473 Yes 14 3 2
## 474 No 13 3 2
## 475 No 16 3 4
## 476 Yes 21 4 3
## 477 No 19 3 4
## 478 No 12 3 1
## 479 No 13 3 3
## 480 Yes 25 4 1
## 481 No 12 3 1
## 482 Yes 19 3 2
## 483 Yes 15 3 2
## 484 Yes 18 3 3
## 485 No 12 3 4
## 486 Yes 11 3 1
## 487 No 13 3 3
## 488 No 17 3 4
## 489 Yes 13 3 4
## 490 No 11 3 4
## 491 Yes 11 3 4
## 492 Yes 11 3 2
## 493 No 11 3 3
## 494 No 19 3 4
## 495 Yes 13 3 1
## 496 No 14 3 3
## 497 No 21 4 4
## 498 Yes 12 3 3
## 499 No 13 3 2
## 500 No 19 3 3
## 501 Yes 14 3 4
## 502 Yes 14 3 2
## 503 Yes 11 3 3
## 504 No 11 3 3
## 505 No 19 3 2
## 506 No 12 3 1
## 507 Yes 13 3 2
## 508 No 11 3 2
## 509 No 17 3 3
## 510 No 12 3 4
## 511 No 11 3 3
## 512 No 21 4 2
## 513 No 13 3 1
## 514 Yes 12 3 3
## 515 No 24 4 3
## 516 No 18 3 4
## 517 Yes 18 3 3
## 518 No 22 4 2
## 519 No 12 3 1
## 520 No 16 3 4
## 521 No 16 3 3
## 522 Yes 17 3 3
## 523 No 17 3 3
## 524 Yes 17 3 3
## 525 No 17 3 1
## 526 No 12 3 1
## 527 Yes 11 3 1
## 528 Yes 12 3 4
## 529 No 18 3 1
## 530 Yes 18 3 1
## 531 No 18 3 1
## 532 No 13 3 1
## 533 No 24 4 3
## 534 No 18 3 2
## 535 Yes 11 3 4
## 536 No 14 3 4
## 537 No 16 3 3
## 538 Yes 11 3 3
## 539 Yes 16 3 2
## 540 No 13 3 4
## 541 No 13 3 1
## 542 No 19 3 2
## 543 Yes 15 3 4
## 544 No 14 3 2
## 545 No 11 3 3
## 546 No 12 3 2
## 547 No 11 3 3
## 548 No 14 3 4
## 549 No 17 3 3
## 550 No 16 3 1
## 551 No 12 3 2
## 552 No 13 3 4
## 553 No 21 4 4
## 554 No 17 3 1
## 555 No 14 3 4
## 556 Yes 11 3 3
## 557 No 11 3 3
## 558 Yes 13 3 4
## 559 No 13 3 4
## 560 No 17 3 2
## 561 No 12 3 3
## 562 Yes 14 3 2
## 563 No 14 3 2
## 564 No 20 4 3
## 565 No 21 4 3
## 566 Yes 12 3 3
## 567 No 13 3 4
## 568 No 11 3 4
## 569 No 17 3 1
## 570 Yes 18 3 4
## 571 No 21 4 3
## 572 No 19 3 1
## 573 Yes 12 3 3
## 574 No 21 4 3
## 575 No 20 4 4
## 576 No 18 3 3
## 577 No 14 3 1
## 578 No 14 3 4
## 579 No 13 3 4
## 580 Yes 11 3 2
## 581 No 11 3 3
## 582 No 14 3 2
## 583 Yes 16 3 3
## 584 Yes 20 4 1
## 585 Yes 13 3 4
## 586 Yes 15 3 3
## 587 Yes 12 3 4
## 588 No 23 4 2
## 589 No 18 3 1
## 590 Yes 15 3 3
## 591 Yes 14 3 2
## 592 Yes 23 4 3
## 593 No 15 3 4
## 594 No 14 3 4
## 595 No 13 3 4
## 596 No 22 4 1
## 597 No 16 3 1
## 598 No 22 4 2
## 599 Yes 20 4 2
## 600 Yes 16 3 1
## 601 No 13 3 3
## 602 No 14 3 2
## 603 Yes 12 3 3
## 604 No 13 3 1
## 605 No 13 3 3
## 606 No 19 3 3
## 607 No 11 3 4
## 608 Yes 24 4 3
## 609 No 12 3 4
## 610 No 19 3 4
## 611 No 12 3 2
## 612 Yes 17 3 1
## 613 No 20 4 1
## 614 Yes 13 3 2
## 615 No 11 3 1
## 616 No 14 3 3
## 617 No 11 3 4
## 618 Yes 21 4 3
## 619 No 15 3 1
## 620 No 18 3 3
## 621 Yes 18 3 2
## 622 Yes 19 3 3
## 623 No 16 3 3
## 624 Yes 16 3 3
## 625 No 11 3 3
## 626 No 11 3 2
## 627 Yes 12 3 3
## 628 Yes 14 3 2
## 629 No 18 3 2
## 630 No 14 3 2
## 631 No 20 4 2
## 632 No 21 4 1
## 633 Yes 12 3 3
## 634 No 22 4 1
## 635 No 16 3 1
## 636 No 13 3 2
## 637 No 17 3 4
## 638 No 18 3 3
## 639 Yes 11 3 2
## 640 No 11 3 2
## 641 No 22 4 2
## 642 No 12 3 2
## 643 No 15 3 2
## 644 Yes 23 4 2
## 645 No 17 3 1
## 646 No 11 3 1
## 647 No 12 3 4
## 648 No 15 3 3
## 649 No 14 3 2
## 650 Yes 14 3 2
## 651 No 19 3 2
## 652 Yes 18 3 2
## 653 No 17 3 3
## 654 No 21 4 2
## 655 No 16 3 3
## 656 Yes 13 3 3
## 657 No 16 3 4
## 658 No 20 4 1
## 659 Yes 15 3 4
## 660 No 13 3 3
## 661 No 15 3 4
## 662 No 12 3 3
## 663 Yes 12 3 2
## 664 No 20 4 1
## 665 No 20 4 3
## 666 Yes 12 3 4
## 667 No 14 3 4
## 668 No 13 3 1
## 669 No 18 3 1
## 670 No 14 3 1
## 671 No 13 3 1
## 672 No 14 3 3
## 673 No 14 3 4
## 674 Yes 19 3 1
## 675 No 17 3 1
## 676 No 14 3 3
## 677 No 15 3 1
## 678 No 11 3 1
## 679 No 13 3 2
## 680 No 15 3 2
## 681 No 12 3 1
## 682 No 20 4 1
## 683 No 14 3 2
## 684 Yes 16 3 2
## 685 No 11 3 3
## 686 No 25 4 4
## 687 Yes 12 3 1
## 688 No 21 4 4
## 689 No 22 4 3
## 690 No 13 3 4
## 691 No 11 3 3
## 692 Yes 12 3 4
## 693 No 23 4 2
## 694 Yes 23 4 3
## 695 No 14 3 3
## 696 Yes 18 3 1
## 697 No 25 4 2
## 698 No 16 3 4
## 699 Yes 12 3 3
## 700 No 11 3 1
## 701 No 11 3 4
## 702 No 20 4 1
## 703 Yes 11 3 2
## 704 Yes 11 3 3
## 705 No 17 3 4
## 706 No 14 3 4
## 707 No 15 3 2
## 708 No 18 3 4
## 709 No 11 3 2
## 710 No 11 3 4
## 711 Yes 23 4 4
## 712 No 12 3 3
## 713 No 11 3 3
## 714 No 11 3 1
## 715 No 15 3 2
## 716 No 11 3 3
## 717 No 18 3 1
## 718 No 13 3 4
## 719 Yes 24 4 1
## 720 No 17 3 2
## 721 No 15 3 3
## 722 No 17 3 1
## 723 No 14 3 3
## 724 No 11 3 2
## 725 No 13 3 4
## 726 No 12 3 4
## 727 No 15 3 2
## 728 Yes 17 3 4
## 729 Yes 18 3 2
## 730 No 14 3 3
## 731 No 21 4 3
## 732 Yes 18 3 1
## 733 No 12 3 1
## 734 No 12 3 4
## 735 No 13 3 3
## 736 No 14 3 2
## 737 No 11 3 1
## 738 No 17 3 4
## 739 No 16 3 2
## 740 No 14 3 4
## 741 No 18 3 4
## 742 No 21 4 3
## 743 No 17 3 1
## 744 No 13 3 3
## 745 Yes 22 4 2
## 746 No 14 3 2
## 747 Yes 15 3 4
## 748 No 20 4 3
## 749 No 13 3 2
## 750 No 14 3 1
## 751 Yes 14 3 2
## 752 Yes 11 3 1
## 753 Yes 15 3 3
## 754 No 21 4 3
## 755 No 12 3 1
## 756 No 14 3 3
## 757 Yes 25 4 4
## 758 No 14 3 2
## 759 No 19 3 4
## 760 Yes 16 3 1
## 761 Yes 20 4 3
## 762 Yes 11 3 3
## 763 No 13 3 1
## 764 No 12 3 1
## 765 No 19 3 3
## 766 Yes 14 3 1
## 767 No 13 3 3
## 768 No 15 3 3
## 769 No 11 3 3
## 770 No 13 3 4
## 771 No 14 3 3
## 772 No 25 4 3
## 773 No 25 4 4
## 774 Yes 17 3 1
## 775 Yes 14 3 3
## 776 No 18 3 4
## 777 No 21 4 1
## 778 No 12 3 3
## 779 No 20 4 3
## 780 Yes 14 3 2
## 781 Yes 22 4 4
## 782 No 11 3 1
## 783 Yes 11 3 3
## 784 No 12 3 1
## 785 No 11 3 4
## 786 No 11 3 3
## 787 No 11 3 4
## 788 No 15 3 1
## 789 No 12 3 2
## 790 No 18 3 4
## 791 No 25 4 1
## 792 No 15 3 4
## 793 Yes 19 3 4
## 794 No 13 3 2
## 795 Yes 15 3 1
## 796 Yes 11 3 1
## 797 Yes 14 3 2
## 798 Yes 17 3 3
## 799 Yes 25 4 2
## 800 No 20 4 1
## 801 No 19 3 1
## 802 Yes 13 3 3
## 803 Yes 14 3 4
## 804 Yes 22 4 1
## 805 No 12 3 3
## 806 Yes 13 3 4
## 807 No 11 3 2
## 808 No 24 4 1
## 809 No 12 3 2
## 810 Yes 16 3 4
## 811 No 11 3 3
## 812 No 11 3 2
## 813 Yes 12 3 4
## 814 No 11 3 3
## 815 No 19 3 4
## 816 No 13 3 2
## 817 Yes 24 4 2
## 818 No 11 3 4
## 819 No 13 3 4
## 820 Yes 11 3 4
## 821 No 17 3 4
## 822 No 14 3 4
## 823 No 21 4 4
## 824 No 12 3 3
## 825 Yes 12 3 2
## 826 No 17 3 4
## 827 No 13 3 1
## 828 Yes 12 3 4
## 829 No 14 3 2
## 830 No 11 3 3
## 831 Yes 15 3 3
## 832 Yes 19 3 4
## 833 No 12 3 3
## 834 No 25 4 3
## 835 Yes 15 3 4
## 836 No 13 3 4
## 837 Yes 15 3 4
## 838 No 12 3 4
## 839 Yes 12 3 3
## 840 No 12 3 4
## 841 No 13 3 3
## 842 No 13 3 1
## 843 No 22 4 3
## 844 No 11 3 4
## 845 No 12 3 1
## 846 No 12 3 4
## 847 No 14 3 4
## 848 No 17 3 1
## 849 Yes 12 3 1
## 850 No 18 3 1
## 851 Yes 11 3 3
## 852 No 13 3 1
## 853 Yes 12 3 4
## 854 No 19 3 3
## 855 Yes 11 3 4
## 856 No 13 3 1
## 857 Yes 11 3 3
## 858 No 24 4 1
## 859 No 21 4 4
## 860 No 11 3 4
## 861 No 14 3 2
## 862 No 14 3 4
## 863 Yes 13 3 2
## 864 Yes 12 3 4
## 865 Yes 18 3 4
## 866 No 11 3 3
## 867 No 18 3 1
## 868 Yes 17 3 3
## 869 Yes 19 3 3
## 870 No 11 3 4
## 871 No 17 3 4
## 872 No 13 3 4
## 873 No 20 4 3
## 874 Yes 13 3 3
## 875 No 14 3 3
## 876 No 20 4 3
## 877 No 15 3 2
## 878 No 13 3 4
## 879 No 19 3 2
## 880 No 12 3 1
## 881 Yes 14 3 4
## 882 No 17 3 2
## 883 No 15 3 4
## 884 No 16 3 4
## 885 Yes 17 3 1
## 886 No 12 3 4
## 887 No 13 3 4
## 888 No 14 3 2
## 889 No 16 3 3
## 890 No 18 3 1
## 891 No 13 3 3
## 892 No 22 4 3
## 893 Yes 19 3 4
## 894 No 14 3 4
## 895 Yes 18 3 2
## 896 No 14 3 4
## 897 No 19 3 2
## 898 No 11 3 3
## 899 No 16 3 3
## 900 No 17 3 1
## 901 Yes 14 3 3
## 902 No 14 3 4
## 903 No 18 3 4
## 904 No 16 3 2
## 905 No 13 3 1
## 906 No 15 3 3
## 907 No 14 3 4
## 908 No 12 3 3
## 909 No 15 3 1
## 910 No 12 3 3
## 911 Yes 20 4 2
## 912 No 18 3 3
## 913 Yes 14 3 3
## 914 No 12 3 4
## 915 Yes 20 4 2
## 916 No 15 3 4
## 917 No 13 3 4
## 918 No 14 3 1
## 919 No 14 3 1
## 920 No 13 3 3
## 921 No 11 3 1
## 922 Yes 11 3 3
## 923 No 12 3 2
## 924 No 11 3 3
## 925 No 14 3 3
## 926 Yes 14 3 3
## 927 Yes 13 3 3
## 928 No 15 3 3
## 929 No 11 3 1
## 930 Yes 24 4 3
## 931 No 14 3 4
## 932 No 14 3 4
## 933 Yes 15 3 1
## 934 Yes 12 3 2
## 935 No 25 4 2
## 936 No 15 3 3
## 937 No 18 3 3
## 938 No 13 3 1
## 939 No 13 3 3
## 940 Yes 16 3 4
## 941 No 19 3 3
## 942 No 25 4 2
## 943 Yes 11 3 2
## 944 Yes 22 4 3
## 945 No 13 3 2
## 946 No 11 3 2
## 947 No 11 3 3
## 948 No 16 3 4
## 949 No 12 3 3
## 950 No 13 3 2
## 951 No 13 3 1
## 952 No 11 3 4
## 953 No 16 3 1
## 954 No 11 3 4
## 955 Yes 19 3 4
## 956 Yes 11 3 2
## 957 No 11 3 3
## 958 Yes 18 3 4
## 959 No 24 4 3
## 960 No 17 3 3
## 961 Yes 19 3 2
## 962 Yes 15 3 4
## 963 No 11 3 3
## 964 Yes 19 3 1
## 965 No 15 3 3
## 966 No 18 3 4
## 967 No 20 4 3
## 968 No 25 4 4
## 969 No 14 3 4
## 970 No 12 3 3
## 971 Yes 18 3 3
## 972 No 22 4 3
## 973 Yes 15 3 2
## 974 Yes 16 3 4
## 975 No 18 3 3
## 976 No 12 3 2
## 977 Yes 13 3 4
## 978 Yes 11 3 4
## 979 Yes 22 4 4
## 980 Yes 12 3 3
## 981 Yes 18 3 3
## 982 No 13 3 4
## 983 No 13 3 3
## 984 No 18 3 1
## 985 Yes 13 3 3
## 986 No 18 3 3
## 987 Yes 21 4 1
## 988 No 22 4 4
## 989 No 11 3 3
## 990 No 13 3 3
## 991 No 15 3 1
## 992 Yes 14 3 3
## 993 Yes 16 3 3
## 994 No 17 3 3
## 995 No 16 3 4
## 996 No 14 3 3
## 997 No 11 3 3
## 998 No 15 3 2
## 999 No 11 3 3
## 1000 No 15 3 3
## 1001 No 11 3 4
## 1002 No 13 3 3
## 1003 Yes 12 3 4
## 1004 Yes 12 3 3
## 1005 No 14 3 4
## 1006 No 18 3 2
## 1007 Yes 17 3 1
## 1008 No 14 3 2
## 1009 No 19 3 4
## 1010 No 15 3 3
## 1011 No 18 3 3
## 1012 No 13 3 2
## 1013 Yes 15 3 4
## 1014 No 20 4 1
## 1015 No 12 3 1
## 1016 No 18 3 3
## 1017 No 19 3 1
## 1018 No 12 3 2
## 1019 No 21 4 4
## 1020 No 21 4 3
## 1021 No 13 3 3
## 1022 No 17 3 4
## 1023 No 22 4 4
## 1024 No 14 3 2
## 1025 Yes 20 4 4
## 1026 No 12 3 3
## 1027 No 20 4 3
## 1028 Yes 12 3 3
## 1029 No 14 3 4
## 1030 Yes 13 3 3
## 1031 Yes 16 3 2
## 1032 No 14 3 2
## 1033 No 11 3 3
## 1034 Yes 13 3 1
## 1035 Yes 13 3 2
## 1036 No 16 3 4
## 1037 Yes 24 4 1
## 1038 Yes 13 3 4
## 1039 Yes 11 3 3
## 1040 No 16 3 4
## 1041 No 13 3 3
## 1042 No 12 3 3
## 1043 No 19 3 1
## 1044 No 12 3 3
## 1045 No 11 3 2
## 1046 No 23 4 4
## 1047 No 16 3 2
## 1048 No 18 3 4
## 1049 Yes 12 3 1
## 1050 Yes 18 3 3
## 1051 No 14 3 1
## 1052 No 12 3 4
## 1053 No 15 3 4
## 1054 No 11 3 3
## 1055 No 11 3 3
## 1056 No 20 4 4
## 1057 No 11 3 3
## 1058 No 12 3 1
## 1059 No 17 3 1
## 1060 No 11 3 1
## 1061 Yes 12 3 2
## 1062 No 15 3 4
## 1063 No 11 3 4
## 1064 No 16 3 1
## 1065 No 11 3 3
## 1066 No 17 3 3
## 1067 Yes 15 3 3
## 1068 No 12 3 4
## 1069 Yes 11 3 2
## 1070 No 11 3 3
## 1071 No 17 3 2
## 1072 No 11 3 3
## 1073 No 14 3 3
## 1074 Yes 21 4 3
## 1075 Yes 20 4 4
## 1076 Yes 23 4 3
## 1077 No 16 3 4
## 1078 No 15 3 4
## 1079 No 12 3 2
## 1080 No 11 3 3
## 1081 No 22 4 3
## 1082 No 14 3 1
## 1083 Yes 12 3 4
## 1084 No 12 3 3
## 1085 No 16 3 2
## 1086 Yes 25 4 2
## 1087 No 18 3 1
## 1088 Yes 15 3 4
## 1089 No 12 3 3
## 1090 No 20 4 1
## 1091 No 13 3 3
## 1092 No 14 3 4
## 1093 No 11 3 4
## 1094 No 13 3 3
## 1095 No 22 4 1
## 1096 Yes 12 3 3
## 1097 Yes 11 3 2
## 1098 Yes 14 3 4
## 1099 Yes 22 4 3
## 1100 No 12 3 3
## 1101 Yes 12 3 4
## 1102 Yes 18 3 4
## 1103 No 14 3 4
## 1104 No 15 3 3
## 1105 Yes 11 3 3
## 1106 No 13 3 3
## 1107 No 11 3 4
## 1108 No 13 3 3
## 1109 Yes 14 3 3
## 1110 No 23 4 4
## 1111 No 12 3 4
## 1112 No 14 3 2
## 1113 Yes 11 3 4
## 1114 Yes 16 3 4
## 1115 No 11 3 1
## 1116 Yes 13 3 1
## 1117 Yes 13 3 3
## 1118 Yes 13 3 3
## 1119 No 11 3 1
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## 1122 No 22 4 2
## 1123 No 15 3 2
## 1124 No 11 3 3
## 1125 No 12 3 4
## 1126 No 15 3 4
## 1127 No 17 3 4
## 1128 Yes 18 3 3
## 1129 Yes 12 3 2
## 1130 No 11 3 1
## 1131 No 13 3 4
## 1132 Yes 19 3 1
## 1133 No 11 3 4
## 1134 No 12 3 4
## 1135 No 18 3 4
## 1136 No 20 4 4
## 1137 No 15 3 3
## 1138 No 22 4 4
## 1139 Yes 23 4 2
## 1140 Yes 11 3 3
## 1141 Yes 20 4 1
## 1142 Yes 21 4 3
## 1143 No 19 3 2
## 1144 No 15 3 4
## 1145 No 12 3 3
## 1146 No 14 3 2
## 1147 No 12 3 2
## 1148 No 17 3 2
## 1149 No 13 3 3
## 1150 No 15 3 3
## 1151 No 12 3 1
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## 1153 No 11 3 2
## 1154 No 19 3 2
## 1155 No 11 3 1
## 1156 Yes 11 3 1
## 1157 Yes 13 3 3
## 1158 Yes 12 3 2
## 1159 Yes 22 4 4
## 1160 No 20 4 4
## 1161 No 12 3 4
## 1162 Yes 13 3 2
## 1163 No 12 3 2
## 1164 No 14 3 4
## 1165 No 21 4 1
## 1166 No 21 4 3
## 1167 No 16 3 1
## 1168 No 12 3 3
## 1169 No 11 3 3
## 1170 Yes 22 4 1
## 1171 No 20 4 3
## 1172 No 18 3 1
## 1173 Yes 17 3 3
## 1174 No 22 4 2
## 1175 No 11 3 1
## 1176 No 14 3 3
## 1177 No 13 3 1
## 1178 No 20 4 3
## 1179 No 25 4 3
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## 1181 No 11 3 1
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## 1183 No 14 3 1
## 1184 No 19 3 2
## 1185 No 19 3 2
## 1186 Yes 19 3 1
## 1187 No 17 3 4
## 1188 No 14 3 3
## 1189 No 22 4 4
## 1190 No 17 3 2
## 1191 No 13 3 3
## 1192 No 12 3 2
## 1193 No 16 3 4
## 1194 Yes 20 4 2
## 1195 No 14 3 2
## 1196 No 14 3 3
## 1197 Yes 20 4 3
## 1198 Yes 13 3 4
## 1199 No 12 3 4
## 1200 No 13 3 2
## 1201 No 12 3 4
## 1202 No 12 3 3
## 1203 No 14 3 3
## 1204 No 19 3 3
## 1205 No 13 3 2
## 1206 No 17 3 3
## 1207 Yes 13 3 3
## 1208 No 16 3 3
## 1209 Yes 13 3 3
## 1210 No 13 3 4
## 1211 No 14 3 2
## 1212 Yes 11 3 2
## 1213 No 17 3 4
## 1214 No 12 3 4
## 1215 No 14 3 2
## 1216 No 14 3 2
## 1217 No 25 4 3
## 1218 No 13 3 1
## 1219 No 22 4 2
## 1220 Yes 20 4 1
## 1221 No 14 3 1
## 1222 No 19 3 4
## 1223 No 14 3 2
## 1224 No 16 3 1
## 1225 No 25 4 3
## 1226 Yes 13 3 3
## 1227 No 15 3 3
## 1228 No 12 3 1
## 1229 Yes 13 3 2
## 1230 No 13 3 3
## 1231 Yes 14 3 3
## 1232 No 15 3 4
## 1233 No 16 3 3
## 1234 Yes 14 3 4
## 1235 No 19 3 2
## 1236 Yes 11 3 3
## 1237 No 16 3 3
## 1238 No 15 3 4
## 1239 No 13 3 1
## 1240 No 19 3 4
## 1241 No 14 3 2
## 1242 No 11 3 1
## 1243 No 11 3 4
## 1244 No 18 3 2
## 1245 No 12 3 1
## 1246 No 19 3 4
## 1247 No 13 3 2
## 1248 No 22 4 2
## 1249 Yes 11 3 2
## 1250 Yes 12 3 4
## 1251 No 22 4 2
## 1252 No 14 3 2
## 1253 No 15 3 1
## 1254 Yes 14 3 3
## 1255 No 15 3 3
## 1256 No 21 4 3
## 1257 No 11 3 1
## 1258 No 12 3 2
## 1259 Yes 16 3 4
## 1260 Yes 12 3 4
## 1261 No 19 3 1
## 1262 No 17 3 1
## 1263 No 15 3 3
## 1264 No 15 3 3
## 1265 No 11 3 3
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## 1267 No 14 3 2
## 1268 No 13 3 2
## 1269 No 22 4 3
## 1270 Yes 13 3 4
## 1271 No 14 3 1
## 1272 Yes 11 3 3
## 1273 Yes 11 3 3
## 1274 No 13 3 4
## 1275 No 15 3 2
## 1276 Yes 13 3 3
## 1277 No 13 3 4
## 1278 No 13 3 1
## 1279 Yes 20 4 2
## 1280 No 13 3 3
## 1281 No 23 4 1
## 1282 Yes 19 3 3
## 1283 No 15 3 2
## 1284 No 18 3 4
## 1285 No 13 3 1
## 1286 Yes 14 3 1
## 1287 Yes 11 3 2
## 1288 No 12 3 3
## 1289 Yes 13 3 3
## 1290 Yes 16 3 4
## 1291 No 11 3 3
## 1292 No 16 3 4
## 1293 Yes 19 3 4
## 1294 No 12 3 4
## 1295 Yes 13 3 2
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## 1297 No 17 3 2
## 1298 No 19 3 4
## 1299 No 13 3 2
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## 1303 No 13 3 3
## 1304 No 13 3 3
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## 1306 Yes 12 3 3
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## 1308 No 14 3 2
## 1309 No 18 3 2
## 1310 Yes 20 4 4
## 1311 No 11 3 2
## 1312 No 15 3 3
## 1313 Yes 11 3 4
## 1314 No 17 3 3
## 1315 No 15 3 3
## 1316 No 12 3 3
## 1317 Yes 11 3 3
## 1318 No 13 3 1
## 1319 No 12 3 3
## 1320 No 15 3 4
## 1321 No 19 3 2
## 1322 No 15 3 2
## 1323 No 14 3 2
## 1324 No 23 4 4
## 1325 No 13 3 2
## 1326 No 13 3 3
## 1327 No 12 3 4
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## 1329 No 14 3 1
## 1330 No 14 3 4
## 1331 Yes 13 3 1
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## 1338 No 15 3 3
## 1339 No 21 4 3
## 1340 No 12 3 1
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## 1345 No 14 3 3
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## 1347 No 11 3 1
## 1348 No 11 3 2
## 1349 Yes 13 3 4
## 1350 Yes 14 3 4
## 1351 No 16 3 2
## 1352 No 13 3 1
## 1353 Yes 14 3 4
## 1354 No 18 3 2
## 1355 No 13 3 3
## 1356 No 13 3 3
## 1357 No 19 3 3
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## 1359 No 13 3 3
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## 1361 No 24 4 1
## 1362 No 13 3 1
## 1363 No 23 4 3
## 1364 Yes 21 4 2
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## 1366 No 14 3 4
## 1367 No 14 3 2
## 1368 No 11 3 4
## 1369 No 14 3 1
## 1370 No 15 3 4
## 1371 No 14 3 2
## 1372 No 12 3 2
## 1373 No 17 3 4
## 1374 No 16 3 3
## 1375 No 16 3 3
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## 1378 No 11 3 1
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## 1381 No 14 3 4
## 1382 No 12 3 4
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## 1388 Yes 13 3 2
## 1389 Yes 12 3 3
## 1390 Yes 11 3 4
## 1391 Yes 14 3 1
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## 1393 Yes 14 3 3
## 1394 No 12 3 2
## 1395 No 12 3 4
## 1396 No 12 3 2
## 1397 No 14 3 1
## 1398 No 11 3 3
## 1399 No 13 3 3
## 1400 No 15 3 2
## 1401 No 14 3 2
## 1402 No 12 3 3
## 1403 No 18 3 4
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## 1405 No 18 3 3
## 1406 No 14 3 4
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## 1408 No 11 3 2
## 1409 No 14 3 4
## 1410 No 16 3 4
## 1411 No 12 3 4
## 1412 Yes 14 3 3
## 1413 Yes 12 3 4
## 1414 Yes 14 3 1
## 1415 No 23 4 2
## 1416 No 19 3 4
## 1417 Yes 12 3 4
## 1418 No 11 3 1
## 1419 Yes 13 3 4
## 1420 Yes 18 3 4
## 1421 Yes 13 3 2
## 1422 No 22 4 3
## 1423 No 21 4 2
## 1424 No 23 4 4
## 1425 No 17 3 4
## 1426 No 21 4 1
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## 1428 No 20 4 2
## 1429 No 19 3 2
## 1430 No 15 3 1
## 1431 No 12 3 1
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## 1433 No 13 3 2
## 1434 No 22 4 2
## 1435 No 22 4 1
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## 1437 No 20 4 2
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## 1439 No 12 3 3
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## 1441 No 21 4 2
## 1442 No 11 3 4
## 1443 No 14 3 2
## 1444 No 14 3 2
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## 1447 No 12 3 1
## 1448 No 12 3 2
## 1449 No 15 3 1
## 1450 No 19 3 1
## 1451 No 11 3 4
## 1452 No 20 4 2
## 1453 No 12 3 4
## 1454 Yes 11 3 4
## 1455 No 14 3 3
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## 1457 Yes 19 3 3
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## 1459 No 13 3 3
## 1460 No 20 4 4
## 1461 Yes 11 3 3
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## 1463 No 13 3 2
## 1464 No 17 3 4
## 1465 Yes 14 3 1
## 1466 No 20 4 3
## 1467 No 20 4 4
## 1468 No 11 3 2
## 1469 No 12 3 3
## 1470 Yes 12 3 1
## StandardHours StockOptionLevel TotalWorkingYears
## 1 80 1 8
## 2 80 0 21
## 3 80 0 10
## 4 80 2 14
## 5 80 0 6
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## 1470 7 1 7nrow(allData)## [1] 1470let’s do some feature engineering based on what is needed to improve the attrition prediction results (% attrition one way or another)
allData$BusinessTravelFrequently = "No"
allData$BusinessTravelFrequently[allData$BusinessTravel=="Travel_Frequently"] <- "Yes"
allData$BusinessTravelFrequently = as.factor(allData$BusinessTravelFrequently)
allData <- allData %>% mutate(LowEducation = case_when(
Education > 4 ~ "No",
Education <= 4 ~ "Yes"))
allData$LowEducation = as.factor(allData$LowEducation)
allData <- allData %>% mutate(LowEnvironmentalSatisfaction = case_when(
EnvironmentSatisfaction > 3 ~ "No",
EnvironmentSatisfaction <= 3 ~ "Yes"))
allData$LowEnvironmentalSatisfaction = as.factor(allData$LowEnvironmentalSatisfaction)
allData <- allData %>% mutate(LowJobLevel = case_when(
JobLevel == 1 ~ "Yes",
JobLevel > 1 ~ "No"))
allData$LowJobLevel = as.factor(allData$LowJobLevel)
allData <- allData %>% mutate(JobRoleSales = case_when(
JobRole == "Sales Representative" ~ "Yes",
JobLevel != "Sales Representative" ~ "No"))
allData$JobRoleSales = as.factor(allData$JobRoleSales)
allData <- allData %>% mutate(HighCompaniesWorked = case_when(
NumCompaniesWorked > 5 ~ "Yes",
Education <= 5 ~ "No"))
allData$HighCompaniesWorked = as.factor(allData$HighCompaniesWorked)
allData <- allData %>% mutate(LowWorkLifeBalance = case_when(
WorkLifeBalance == 1 ~ "Yes",
WorkLifeBalance > 1 ~ "No"))
allData$LowWorkLifeBalance = as.factor(allData$LowWorkLifeBalance)
allData <- allData %>% mutate(JobRoleMed = case_when(
JobRole == "Human Resources" ~ "Yes",
JobRole == "Laboratory Technician" ~ "Yes",
JobRole == "Research Scientist" ~ "Yes",
JobRole == "Sales Executive" ~ "Yes",
TRUE ~ "No"))
allData$JobRoleMed = as.factor(allData$JobRoleMed)
str(allData)## 'data.frame': 1470 obs. of 44 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction : int 3 1 3 3 3 3 1 3 4 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...
## $ BusinessTravelFrequently : Factor w/ 2 levels "No","Yes": 1 1 2 1 2 2 1 1 1 2 ...
## $ LowEducation : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 1 2 2 2 ...
## $ LowEnvironmentalSatisfaction: Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 1 2 1 ...
## $ LowJobLevel : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 1 2 1 ...
## $ JobRoleSales : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 2 1 ...
## $ HighCompaniesWorked : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ LowWorkLifeBalance : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ JobRoleMed : Factor w/ 2 levels "No","Yes": 2 1 1 2 2 1 2 2 1 1 ...let’s fix some variables that are numeric but should be factors
allData$Education <- as.factor(allData$Education)
allData$EnvironmentSatisfaction <- as.factor(allData$EnvironmentSatisfaction)
allData$JobInvolvement <- as.factor(allData$JobInvolvement)
allData$JobLevel <- as.factor(allData$JobInvolvement)
allData$JobSatisfaction <- as.factor(allData$JobSatisfaction)
allData$PerformanceRating <- as.factor(allData$PerformanceRating)
allData$RelationshipSatisfaction <- as.factor(allData$RelationshipSatisfaction)
allData$StockOptionLevel <- as.factor(allData$StockOptionLevel)
allData$WorkLifeBalance <- as.factor(allData$WorkLifeBalance)let’s check if our data looks reasonable and that we are not missing any fields?
we see that we are missing 300 attrition and 300 monthly income - that’s what we expect (we had separate DF’s of just those sizes)
There is NO missing data here, yay! One problem down.
there data looks good, Some comments:
Educaition is a number, but maybe should be a factor?
employee count is always 1? Useless
employee number is an identfier and we should not be using it for prediction probably? It could indicate how long the employee has been there but that should be covered elsewhere
Environmental satisfaction is also a numeric - could be a factor?
Other fields that could be factors - JobInvolvement, JobLevel, JobSatisfaction
what is the difference between monthlyIncome and MonthlyRate
WHat is numcompaniesWorked? Is this past employment? Either way it’s definitely numeric not a factor
What is stockOptionLevel?
What is “WOrkLifeBalance”? That’s really odd - how could they even measure that?!? OK…
I guess you could be in a different role, but not prompted
rather than use the original datasets as we got them, let’s take our data from the combined dataset, because that will sure all the columns are identifical
train <- allData[(! is.na(allData$Attrition)) ,]
train <- train[(! is.na(train$MonthlyIncome )), ]
nrow(train) ## [1] 870# back to our original 870
predictAttr = allData[ is.na(allData$Attrition ),]
nrow(predictAttr)## [1] 300predictMonthIncome = allData[ is.na(allData$MonthlyIncome),]
nrow(predictMonthIncome)## [1] 300trainAndAttrit =rbind.fill(train, predictAttr)
trainAndMonthlyIncome = rbind.fill(train, predictMonthIncome)for reference here are the columns:
1 - ID
2 - Age
3 - Attrition
4 - Business Travel
5 - DailyRate
6 - Department
7 - DistanceFromHome
8 - Education
9 - EducationField
10 - Employee Count
11 - EmployeeNumber
12 - EnvironmentSatisfaction
13 - Gender
14 - Hourly Rate
15 - JobInvolvement
16 - JobLevel
17 - Job ROle
18 - Job Satisfcation
19 - Marital Status
20 - MonthlyIncome
21 - MonthlyRate
22 NumCompaniesWorked
23 - Over18
24 - overTime
25 - PercentSalaryHike
26 - PerformanceRating
27 - RelationSatisfaction
28 - standardHours
29 - StockOptionlevel
30 - TotalWorkingYears
31 - TrainingTimesLastYear
32 - WorkLifeBalance
33 - YEarsAtCompany
34 - YearsInCurrentRole
35 - YearsSinceLastPromotion
36 - YearsWithCurrManager -
All of these fields look reasonable to use except :
1 - ID, 3 - Attrition (cant use the field to predict itself), 10 - Employee Count (it’s always 1 so why bother),
11 - EmployeeNumber should be a random # - it could have some usefulness or it could lead us astray so let’s try without
also the variables 23 - Over18 is always the same value…
and the variable 28 - StandardHours also has the same value always
# create a list of the fields we want to use taking out the ones we dont
goodToPredictAttr = c(2,3,4,5,6,7,8,9,10,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,29,30,31,32,33,34,35)
attrField = c(3)
head(train[goodToPredictAttr])## Age Attrition BusinessTravel DailyRate Department
## 1 32 No Travel_Rarely 117 Sales
## 2 40 No Travel_Rarely 1308 Research & Development
## 3 35 No Travel_Frequently 200 Research & Development
## 4 32 No Travel_Rarely 801 Sales
## 5 24 No Travel_Frequently 567 Research & Development
## 6 27 No Travel_Frequently 294 Research & Development
## DistanceFromHome Education EducationField EmployeeCount
## 1 13 4 Life Sciences 1
## 2 14 3 Medical 1
## 3 18 2 Life Sciences 1
## 4 1 4 Marketing 1
## 5 2 1 Technical Degree 1
## 6 10 2 Life Sciences 1
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 3
## 2 3 Male 44 2 2
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 3
## 6 4 Male 32 3 3
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## 6 Manufacturing Director 1 Divorced 8793
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike
## 1 9250 2 No 11
## 2 17544 1 No 14
## 3 19944 2 No 11
## 4 24032 1 No 19
## 5 17218 1 Yes 13
## 6 4809 1 No 21
## PerformanceRating RelationshipSatisfaction StockOptionLevel
## 1 3 3 1
## 2 3 1 0
## 3 3 3 0
## 4 3 3 2
## 5 3 3 0
## 6 4 3 2
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 8 3 2 5
## 2 21 2 4 20
## 3 10 2 3 2
## 4 14 3 3 14
## 5 6 2 3 6
## 6 9 4 2 9
## YearsInCurrentRole YearsSinceLastPromotion
## 1 2 0
## 2 7 4
## 3 2 2
## 4 10 5
## 5 3 1
## 6 7 1lets check the correlation of our variables
This shows correlation between:
HIGH - MothlyIncome + TotalWorkingYears, age + TotalWorkingYears, Joblevel + TotalWorkingYears, PercentSalaryHike + PerformanceRating, YearsAtCompany + YearsAtCurrentRole, YearsAtCompany + YearsWithCurrManager
MED - Age + JobLevel, JobLevel + YearsAtCompany , Monthly Income with TraingTimeLastYear, YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithCurrManager
suppressMessages(library(corrplot))
dataHasMonthlyIncome <- allData %>% filter(!is.na(allData$MonthlyIncome))
numVars <- sapply(dataHasMonthlyIncome, is.numeric)
correlationMatrix <- cor(dataHasMonthlyIncome[,numVars])## Warning in cor(dataHasMonthlyIncome[, numVars]): the standard deviation is
## zero# same thing but now with p value
corrplot(correlationMatrix,main='\nCorrelation Plot with P Values', type="upper",
sig.level = 0.05, tl.cex = 0.75, number.cex=1)
numVars## ID Age
## TRUE TRUE
## Attrition BusinessTravel
## FALSE FALSE
## DailyRate Department
## TRUE FALSE
## DistanceFromHome Education
## TRUE FALSE
## EducationField EmployeeCount
## FALSE TRUE
## EmployeeNumber EnvironmentSatisfaction
## TRUE FALSE
## Gender HourlyRate
## FALSE TRUE
## JobInvolvement JobLevel
## FALSE FALSE
## JobRole JobSatisfaction
## FALSE FALSE
## MaritalStatus MonthlyIncome
## FALSE TRUE
## MonthlyRate NumCompaniesWorked
## TRUE TRUE
## Over18 OverTime
## FALSE FALSE
## PercentSalaryHike PerformanceRating
## TRUE FALSE
## RelationshipSatisfaction StandardHours
## FALSE TRUE
## StockOptionLevel TotalWorkingYears
## FALSE TRUE
## TrainingTimesLastYear WorkLifeBalance
## TRUE FALSE
## YearsAtCompany YearsInCurrentRole
## TRUE TRUE
## YearsSinceLastPromotion YearsWithCurrManager
## TRUE TRUE
## BusinessTravelFrequently LowEducation
## FALSE FALSE
## LowEnvironmentalSatisfaction LowJobLevel
## FALSE FALSE
## JobRoleSales HighCompaniesWorked
## FALSE FALSE
## LowWorkLifeBalance JobRoleMed
## FALSE FALSEageAndYearCars = c("Age", "MonthlyIncome","TotalWorkingYears", "YearsAtCompany", "YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager")
correlationMatrixSomeNumeric <- cor(dataHasMonthlyIncome[,ageAndYearCars])
# same thing but now with p value
corrplot(correlationMatrixSomeNumeric,main='\nCorrelation Plot with P Values', type="upper",
sig.level = 0.05, tl.cex = 0.75, number.cex=1)
Do some exploratory data analysis for the relationship between the various variables and the attrition
very significant columns : Department (especially sales has higher),
somewhat significant columns
library(ggthemes)
ggplot(data=train, aes(train$Attrition)) +
geom_bar(aes(y = (..count..)/sum(..count..))) + ylab("percent") + theme_economist() + ggtitle("Histogram of Attrition") + xlab("Attrition") 
ggplot(data=train, aes(x="", y = Attrition, fill=train$Attrition)) +
geom_bar(stat="identity", width=1, positions = position_fill()) +
coord_polar("y", start=0) +
ylab("percent") + theme_economist() + ggtitle("Histogram of Busines Travel") + xlab("Busines Travel") + scale_fill_discrete(name = "Attrition:") + facet_wrap(train$BusinessTravel)## Warning: Ignoring unknown parameters: positions
ggplot(data=train, aes(x=train$DailyRate, fill=train$Attrition)) +
geom_histogram(bins=50) + theme_economist() + ggtitle("Histogram of Daily Rate") + xlab("Daily Rate") + geom_density(alpha=.3) + scale_fill_discrete(name = "Attrition:")
ggplot(data=train, aes(train$Department, fill=train$Attrition)) +
geom_bar(aes(y = (..count..)/sum(..count..)), position="dodge") + ylab("percent") + theme_economist() + ggtitle("Histogram of Department") + xlab("Department") + scale_fill_discrete(name = "Attrition:")
ggplot(data=train, aes(train$Education, fill=train$Attrition)) +
geom_bar(aes(y = (..count..)/sum(..count..)), position="dodge") + ylab("percent") + theme_economist() + ggtitle("Histogram of Education") + xlab("Education") + scale_fill_discrete(name = "Attrition:")
lets make a nicer chart of just attrition
library(plotly)##
## Attaching package: 'plotly'## The following objects are masked from 'package:plyr':
##
## arrange, mutate, rename, summarise## The following object is masked from 'package:reshape':
##
## rename## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutattritGrouped <- trainAndMonthlyIncome %>% group_by(Attrition) %>% tally() %>%
mutate(percent=round(prop.table(n),2) * 100) %>%
ggplot(aes(x=Attrition, y=percent)) + geom_bar(stat="identity") +
geom_text(aes(x=Attrition, y=0.1, label= sprintf("%.2f%%", percent)),
hjust=0.5, vjust=-3, size=4,
colour="white", fontface="bold") + theme_bw() + labs(x="Employee Attrition", y="Percentage", title="Employee Attrition Percentage") +
theme_economist()
attritGrouped
attritGrouped <- trainAndMonthlyIncome %>% group_by(Attrition) %>% tally()
bp<- ggplot(attritGrouped, aes(x="", y=n, fill=Attrition))+
geom_bar(width = 1, stat = "identity")
pie <- bp + coord_polar("y", start=0)
pie
try to get a nice percentage facet wrap
library(dplyr)
library(datasets)
library(ggplot2)
data(mtcars)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# creating a dataframe
df <- dplyr::group_by(mtcars, .dots = c('cyl', 'am')) %>%
dplyr::summarize(counts = n()) %>%
dplyr::mutate(perc = (counts / sum(counts)) * 100) %>%
dplyr::arrange(desc(perc))
# preparing the plot
ggplot2::ggplot(df, aes('', counts)) +
geom_col(
position = 'fill',
color = 'black',
width = 1,
aes(fill = factor(am))
) +
facet_wrap(~cyl, labeller = "label_both") +
geom_label(
aes(label = paste0(round(perc), "%"), group = factor(am)),
position = position_fill(vjust = 0.5),
color = 'black',
size = 5,
show.legend = FALSE
) +
coord_polar(theta = "y")
# use fuction to show percentages
ShowPieChart <- function(columnBy, columnToShow, titleName)
{
df <- dplyr::group_by(train, .dots = c(columnBy, columnToShow)) %>%
dplyr::summarize(counts = n()) %>%
dplyr::mutate(perc = (counts / sum(counts)) * 100) %>%
dplyr::arrange_(.dots=c(columnBy, columnToShow))
# preparing the plot
ggplot2::ggplot(df, aes('', counts)) +
geom_col(
position = 'fill',
color = 'black',
width = 1,
aes(fill = Attrition)
) +
ggtitle(titleName) +
facet_wrap(paste("~",columnBy), labeller = "label_both") +
geom_label(
aes(label = paste0(round(perc), "%"), group = "Attrition"),
position = position_fill(vjust = 0.5),
color = 'black',
size = 5,
show.legend = FALSE
) + scale_fill_discrete(name = "Attrition:") +
coord_polar(theta = "y")
}
ShowPieChart("BusinessTravel", "Attrition", "Attrition by Business Travel")## Warning: arrange_() is deprecated.
## Please use arrange() instead
##
## The 'programming' vignette or the tidyeval book can help you
## to program with arrange() : https://tidyeval.tidyverse.org
## This warning is displayed once per session.
ShowPieChart("Department", "Attrition", "Attrition by Department")
ShowPieChart("Education", "Attrition", "Attrition by Education")
ShowPieChart("EducationField", "Attrition", "Attrition by Education Field")
ShowPieChart("EnvironmentSatisfaction", "Attrition", "Attrition by Environmental Satisfaction")
ShowPieChart("Gender", "Attrition", "Attrition by Gender")
ShowPieChart("JobLevel", "Attrition", "Attrition by Job Level" )
ShowPieChart("JobRole", "Attrition", "Attrition by Job Role")
ShowPieChart("JobSatisfaction", "Attrition", "Attrition by Job Satisfaction")
ShowPieChart("MaritalStatus", "Attrition", "Attrition by Marital Status")
ShowPieChart("NumCompaniesWorked", "Attrition", "Attrition by Number of COmpanies WOrked")
ShowPieChart("OverTime", "Attrition", "Attritio by Overtime")
ShowPieChart("PerformanceRating", "Attrition", "Attrition by Performance Rating")
ShowPieChart("RelationshipSatisfaction", "Attrition", "Attrition by Relationship Satisfaction")
ShowPieChart("StockOptionLevel", "Attrition", "Attrition by Stock Option Level")
ShowPieChart("WorkLifeBalance", "Attrition", "Attritio by Work Life Balance")
This is a quick but not as pretty review of the continuous variables by Attrition
NiceBoxPlot <- function(columnBy, columnToShow, columnByDesc, columnToShowDesc)
{
ggplot(train, aes(x=train[,columnBy], y=train[,columnToShow])) + geom_boxplot() +
stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
ggtitle(paste(columnByDesc, "by",columnToShowDesc)) + xlab("Attrition") + ylab(columnToShowDesc) +
theme_economist()
}
NiceBoxPlot("Attrition", "DistanceFromHome", "Attrition", "Distance from Home")
NiceBoxPlot("Attrition", "HourlyRate", "Attrition", "Hourly Rate")
NiceBoxPlot("Attrition", "MonthlyIncome", "Attrition", "Monthly Income")
NiceBoxPlot("Attrition", "MonthlyRate", "Attrition", "Monthly Rate")
NiceBoxPlot("Attrition", "NumCompaniesWorked", "Attrition", "Number of Companies Worked")
NiceBoxPlot("Attrition", "PercentSalaryHike", "Attrition", "Percent Salary Hike")
NiceBoxPlot("Attrition", "TotalWorkingYears", "Attrition", "Total Working Years")
NiceBoxPlot("Attrition", "TrainingTimesLastYear", "Attrition", "Training Times Last Year")
NiceBoxPlot("Attrition", "YearsAtCompany", "Attrition", "Years at Company")
NiceBoxPlot("Attrition", "YearsInCurrentRole", "Attrition", "Years in Current Role")
NiceBoxPlot("Attrition", "YearsSinceLastPromotion", "Attrition", "Years since Last Promotion")
NiceBoxPlot("Attrition", "YearsWithCurrManager", "Attrition", "Years with Current Manager")
let’s do further analysis to better understand what could be causing
# look at job role + job level
p<-ggplot(data=trainAndMonthlyIncome, aes(x=JobRole,y = "", fill=Attrition)) +
geom_bar(stat="identity") + facet_grid(JobLevel ~ .) + xlab("Job Level") + ylab("Job Role") + ggtitle("Attrition by Job level and Job Role")
p
# this scales better if saved to a file
ggsave("AttritionByJobLevelJobRole.jpg", plot = p, width = 12, height = 12, units = "in")
# maybe use dodge to make this smaller?
p = ggplot(trainAndMonthlyIncome, aes(JobRole, fill = Attrition)) +
geom_bar(position = "dodge2") + facet_grid(JobLevel ~ .) +
ggtitle("Attrition by Job Level and Job Role") + xlab("Job Role") + ylab("Count by Job Level")
p
ggsave("AttritionByJobLevelJobRoleDodge.jpg", plot = p, width = 15, height = 8, units = "in")
p = ggplot(trainAndMonthlyIncome, aes(JobRole, fill = Attrition)) +
geom_bar(position = "dodge2") + facet_grid(OverTime ~ .) +
ggtitle("Attrition by Overtime and Job Role") + xlab("Job Role") + ylab("Count by Overtime")
p
ggsave("AttritionByOvertimeJobRoleDodge.jpg", plot = p, width = 15, height = 8, units = "in")
p = ggplot(trainAndMonthlyIncome, aes(JobRole, fill = Attrition)) +
geom_bar(position = "dodge2") + facet_grid(JobSatisfaction ~ .) +
ggtitle("Attrition by Job Satisfaction and Job Role") + xlab("Job Satisfaction") + ylab("Count by Overtime")
p
ggsave("AttritionByJobSatisfactionJobRoleDodge.jpg", plot = p, width = 15, height = 8, units = "in")
# stock option level by job role
p = ggplot(trainAndMonthlyIncome, aes(JobRole, fill = Attrition)) +
geom_bar(position = "dodge2") + facet_grid(StockOptionLevel ~ .) +
ggtitle("Attrition by Stock Option Level and Job Role") + xlab("Job Role") + ylab("Count by Stock Option Level")
p
ggsave("AttritionBystockOptionLevelJobRoleDodge.jpg", plot = p, width = 15, height = 8, units = "in") Let’s start off with Naive Bayes and predict attrition
This very basic model with absolutely nothing changed gives us 85% accuracy, 90% sensitivity and 61% specificity.
But we dont know from this what our accuracy is on the validation data set, and 61% is awfully close to 60. Would be nice to increase this higher
suppressMessages(library(e1071))
suppressMessages(library(caret))
modelBayes = naiveBayes(Attrition~.,data = train[goodToPredictAttr])
#model
result = predict(modelBayes,train[goodToPredictAttr])
# result is our predictions, but we have values on the original, let's compare to those
CM = confusionMatrix(table(result,train$Attrition), positive="Yes")
CM## Confusion Matrix and Statistics
##
##
## result No Yes
## No 670 54
## Yes 60 86
##
## Accuracy : 0.869
## 95% CI : (0.8447, 0.8907)
## No Information Rate : 0.8391
## P-Value [Acc > NIR] : 0.00809
##
## Kappa : 0.523
##
## Mcnemar's Test P-Value : 0.63958
##
## Sensitivity : 0.61429
## Specificity : 0.91781
## Pos Pred Value : 0.58904
## Neg Pred Value : 0.92541
## Prevalence : 0.16092
## Detection Rate : 0.09885
## Detection Prevalence : 0.16782
## Balanced Accuracy : 0.76605
##
## 'Positive' Class : Yes
## str(result)## Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 1 2 1 ...length(result)## [1] 870confusion matrix for naive bayes
# and print out a nice confusion matrix
draw_confusion_matrix <- function(cmtrx) {
total <- sum(cmtrx$table)
res <- as.numeric(cmtrx$table)
# Generate color gradients. Palettes come from RColorBrewer.
greenPalette <- c("#F7FCF5","#E5F5E0","#C7E9C0","#A1D99B","#74C476","#41AB5D","#238B45","#006D2C","#00441B")
redPalette <- c("#FFF5F0","#FEE0D2","#FCBBA1","#FC9272","#FB6A4A","#EF3B2C","#CB181D","#A50F15","#67000D")
getColor <- function (greenOrRed = "green", amount = 0) {
if (amount == 0)
return("#FFFFFF")
palette <- greenPalette
if (greenOrRed == "red")
palette <- redPalette
colorRampPalette(palette)(100)[10 + ceiling(90 * amount / total)]
}
# set the basic layout
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX - Training Data', cex.main=2)
# create the matrix
classes = colnames(cmtrx$table)
rect(150, 430, 240, 370, col=getColor("green", res[1]))
text(195, 435, classes[1], cex=1.2)
rect(250, 430, 340, 370, col=getColor("red", res[3]))
text(295, 435, classes[2], cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col=getColor("red", res[2]))
rect(250, 305, 340, 365, col=getColor("green", res[4]))
text(140, 400, classes[1], cex=1.2, srt=90)
text(140, 335, classes[2], cex=1.2, srt=90)
# add in the cmtrx results
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cmtrx$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cmtrx$byClass[1]), 3), cex=1.2)
text(30, 85, names(cmtrx$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cmtrx$byClass[2]), 3), cex=1.2)
text(50, 85, names(cmtrx$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cmtrx$byClass[5]), 3), cex=1.2)
text(70, 85, names(cmtrx$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cmtrx$byClass[6]), 3), cex=1.2)
text(90, 85, names(cmtrx$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cmtrx$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cmtrx$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cmtrx$overall[1]), 3), cex=1.4)
text(70, 35, names(cmtrx$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cmtrx$overall[2]), 3), cex=1.4)
}
draw_confusion_matrix(CM)
# the prior test we created the confusion matrix based on the training data. Let’s so a test/train split and assess
set.seed(1)
splitPerc = .7
iterations = 50
masterAcc = matrix(nrow = iterations, ncol = 1)
masterSpec = matrix(nrow = iterations, ncol = 1)
masterSens = matrix(nrow = iterations, ncol = 1)
for(j in 1:iterations)
{
trainIndices = sample(1:dim(train)[1],round(splitPerc * nrow(train)))
trainData = train[trainIndices,]
testData = train[-trainIndices,]
modelBayes = naiveBayes(Attrition~.,data = trainData[goodToPredictAttr])
predictAttrBayes = predict(modelBayes, testData)
CM = confusionMatrix(table(predictAttrBayes, as.factor(testData$Attrition)),positive="Yes")
masterAcc[j,1] = CM$overall[1]
masterSens[j,1] = CM$byClass[1][[1]] # sensitivity
masterSpec[j,1] = CM$byClass[2][[1]] # specificity
}
MeanAcc = colMeans(masterAcc)
MeanAcc## [1] 0.8449808MeanSens = colMeans(masterSens)
MeanSens## [1] 0.5359252MeanSpec = colMeans(masterSpec)
MeanSpec## [1] 0.9057158What we really want to do though, is use a single variable for each category for these category variables. This page refers to this as creatig a “Contrast Matrix”
first some attempts at doing many comparisons at once. These work OK, but ultimately are not very high production values
numVars <- sapply(allData, is.numeric)
head(train)## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## 6 10 2 Life Sciences 1 733
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 3
## 2 3 Male 44 2 2
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 3
## 6 4 Male 32 3 3
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## 6 Manufacturing Director 1 Divorced 8793
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 9250 2 Y No 11
## 2 17544 1 Y No 14
## 3 19944 2 Y No 11
## 4 24032 1 Y No 19
## 5 17218 1 Y Yes 13
## 6 4809 1 Y No 21
## PerformanceRating RelationshipSatisfaction StandardHours
## 1 3 3 80
## 2 3 1 80
## 3 3 3 80
## 4 3 3 80
## 5 3 3 80
## 6 4 3 80
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 1 1 8 3 2
## 2 0 21 2 4
## 3 0 10 2 3
## 4 2 14 3 3
## 5 0 6 2 3
## 6 2 9 4 2
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 1 5 2 0
## 2 20 7 4
## 3 2 2 2
## 4 14 10 5
## 5 6 3 1
## 6 9 7 1
## YearsWithCurrManager BusinessTravelFrequently LowEducation
## 1 3 No Yes
## 2 9 No Yes
## 3 2 Yes Yes
## 4 7 No Yes
## 5 3 Yes Yes
## 6 7 Yes Yes
## LowEnvironmentalSatisfaction LowJobLevel JobRoleSales
## 1 Yes No No
## 2 Yes No No
## 3 Yes No No
## 4 Yes No No
## 5 Yes Yes No
## 6 No No No
## HighCompaniesWorked LowWorkLifeBalance JobRoleMed
## 1 No No Yes
## 2 No No No
## 3 No No No
## 4 No No Yes
## 5 No No Yes
## 6 No No Nocaret::featurePlot(x = train[,c(2,5,7)], y = train$MonthlyIncome, plot="pairs", auto.key = list(columns=3))
# this is interesting but it's just too small and hard to modify the labels
suppressMessages(library(tidyr))
suppressMessages(library(dplyr))
notNumeric <- function(x)
{
! is.numeric(x)
}
select_if(train,is.numeric) %>%
gather(-MonthlyIncome, key = "var", value = "value") %>%
ggplot(aes(x = value, y = MonthlyIncome)) +
geom_point() +
facet_wrap(~ var, scales = "free") +
theme_economist()
analysis at a field by field level
# business travel
suppressMessages(library(ggplot2))
suppressMessages(library(ggthemes))
fill <- "#4271AE"
line <- "#1F3552"
ggplot(data=trainAndAttrit, aes(trainAndAttrit$MonthlyIncome)) +
geom_histogram() + theme_economist() + ggtitle("Histogram of Monthly Income") + xlab("Monthly Income")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
paste("Mean monthly income is:",mean(trainAndAttrit$MonthlyIncome))## [1] "Mean monthly income is: 6573.11965811966"ggplot(trainAndAttrit, aes(x=Age, y=MonthlyIncome), xlab="Age", ylab="Monthly Income") + ggtitle("Age vs Monthly Income") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x = BusinessTravel, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Business Travel") +
ggtitle("Boxplot of Business Travel") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=DailyRate, y=MonthlyIncome), xlab="Daily Rate", ylab="Monthly Income") + ggtitle("Monthly Income vs Daily Rate") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x = Department, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Department") +
ggtitle("Boxplot of Department") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=DistanceFromHome, y=MonthlyIncome), xlab="Distance From Home", ylab="Monthly Income") + ggtitle("Monthly Income vs Distance From Home") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x = EducationField, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Eduction Field") +
ggtitle("Boxplot Monthly Income by Eduction Field") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = Education, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Eduction") +
ggtitle("Boxplot Monthly Income by Education") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = EnvironmentSatisfaction, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Environmental Satisfaction") +
ggtitle("Boxplot Monthly Income by Environmental Satisfaction") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = Gender, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Gender") +
ggtitle("Boxplot Monthly Income by Gender") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=HourlyRate, y=MonthlyIncome), xlab="Hourly Rate", ylab="Monthly Income") + ggtitle("Monthly Income vs Hourly Rate") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x = JobInvolvement, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Job Involvement") +
ggtitle("Boxplot Monthly Income by Job Involvement") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = JobLevel, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Job Level") +
ggtitle("Boxplot Monthly Income by Job Level") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = JobSatisfaction, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Job Satisfaction") +
ggtitle("Boxplot Monthly Income by Job Satisfaction") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = MaritalStatus, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Marital Status") +
ggtitle("Boxplot Monthly Income by Marital Status") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=MonthlyRate, y=MonthlyIncome), xlab="Monthly Rate", ylab="Monthly Income") + ggtitle("Monthly Income vs Monthly Rate") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x=NumCompaniesWorked, y=MonthlyIncome), xlab="Number of Companies Worked", ylab="Monthly Income") + ggtitle("Monthly Income vs Number of Companies Worked") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x = OverTime, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Overtime") +
ggtitle("Boxplot Monthly Income by overtime") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=PercentSalaryHike, y=MonthlyIncome), xlab="Percentage Salary Hike", ylab="Monthly Income") + ggtitle("Monthly Income vs Percentage Salary Hike") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x = PerformanceRating, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Performance Rating") +
ggtitle("Boxplot Monthly Income by Performance Rating") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = RelationshipSatisfaction, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Relationship Satisfaction") +
ggtitle("Boxplot Monthly Income by Relationship Satisfaction") +
theme_economist() 
ggplot(trainAndAttrit, aes(x = StockOptionLevel, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Stock Option Level") +
ggtitle("Boxplot Monthly Income by Stock Level") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=TotalWorkingYears, y=MonthlyIncome), xlab="Total Working Years", ylab="Monthly Income") + ggtitle("Monthly Income vs Total Working Years") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x=TrainingTimesLastYear, y=MonthlyIncome), xlab="Training Times Last Year", ylab="Monthly Income") + ggtitle("Monthly Income vs Training Times Last Year") + geom_point() + geom_smooth(method=loess) + theme_economist()## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 2## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 1
ggplot(trainAndAttrit, aes(x = WorkLifeBalance, y = MonthlyIncome)) +
geom_boxplot() +
scale_y_continuous(name = "Monthly Income") +
scale_x_discrete(name = "Work Life Balance") +
ggtitle("Boxplot Monthly Income by Work Life Balance") +
theme_economist() 
ggplot(trainAndAttrit, aes(x=YearsAtCompany, y=MonthlyIncome), xlab="Years at Company", ylab="Monthly Income") + ggtitle("Monthly Income vs Years at Company") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x=YearsInCurrentRole, y=MonthlyIncome), xlab="Years in Current Role", ylab="Monthly Income") + ggtitle("Monthly Income vs Years in Current Role") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x=YearsSinceLastPromotion, y=MonthlyIncome), xlab="Years since Last Promotion", ylab="Monthly Income") + ggtitle("Monthly Income vs Years since Last Promotion") + geom_point() + geom_smooth(method=loess) + theme_economist()
ggplot(trainAndAttrit, aes(x=YearsWithCurrManager, y=MonthlyIncome), xlab="Years with Current Manager", ylab="Monthly Income") + ggtitle("Monthly Income vs Years with Current Manager") + geom_point() + geom_smooth(method=loess) + theme_economist()
# let's check correlation between all these year variables
ageRelated = c(2, 20,30, 33, 34, 35, 36)
pairs(train[,ageRelated], pch=19)
ggplot(trainAndAttrit, aes(x=NumCompaniesWorked, y=TotalWorkingYears), xlab="Number of COmpanies Worked", ylab="Total Working Years") + ggtitle("Number of Companies Worked vs Total Working Years") + xlab("Number of Companies Worked") + ylab("Total Working Years") + geom_point() + geom_smooth(method=loess) + theme_economist()
## analysis results: #### Overall the most correlated fields are : Department, Education, NumCompaniesWorked, StockOptionLevel, TotalWorkingYears, YearsAtCompany (strongly correlated with TotalWorkingYears), YearsInCurrentRole (strongly correlated with TotalWorkingYears), YearsSinceLastPromotion(strongly correlated with TotalWorkingYears), YearsWithCurrentManager( stringly correlated with TotalWorkingYears), Age ( strongly correlated with all the Year field as well. It could be that age is a better choice vs TotalWorkingYears) #### Other considerations : Gender, Marital Status, PercentageSalaryHike (non-linear), PerformanceRating, RelationshipSatisfaction(low), WorkLifeBalance(1 is different) #### not sure what mothlyrate field indicates. It has a non-linear relationship with MonthlyIncome, and it does not appear overly strong # it’s intereting the NumCompaniesWorked does not correlate more with TotalWorkingYears. It does for employees with < 2.5 companies. But after that it’s pretty flat!
linear model
minimum goal is to get RMSE < 3000 on both the training and the validation set
After much trial and error with combinations
# Function that returns Root Mean Squared Error
rmse <- function(error)
{
sqrt(mean(error^2))
}
## since this model does NOT use the attrition field, let's include the data for the employees where we are trying to determine attrition too
trainAndAttrit =rbind.fill(train, predictAttr)
# linear model (after trying many combinations and ruling out variables that failed to improve RMSE and/or had high P values)
linearMod <- lm(trainAndAttrit$MonthlyIncome~Age + JobRole + Department + PerformanceRating + WorkLifeBalance + YearsAtCompany, data=trainAndAttrit)
#
summary(linearMod)##
## Call:
## lm(formula = trainAndAttrit$MonthlyIncome ~ Age + JobRole + Department +
## PerformanceRating + WorkLifeBalance + YearsAtCompany, data = trainAndAttrit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6000.7 -1225.8 -154.8 998.7 6609.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4536.962 752.437 6.030 2.21e-09 ***
## Age 59.730 6.663 8.964 < 2e-16 ***
## JobRoleHuman Resources -3889.820 729.640 -5.331 1.17e-07 ***
## JobRoleLaboratory Technician -3739.670 224.833 -16.633 < 2e-16 ***
## JobRoleManager 8513.764 355.195 23.969 < 2e-16 ***
## JobRoleManufacturing Director -58.057 247.519 -0.235 0.8146
## JobRoleResearch Director 8017.147 287.928 27.844 < 2e-16 ***
## JobRoleResearch Scientist -3757.595 220.849 -17.014 < 2e-16 ***
## JobRoleSales Executive -332.455 492.103 -0.676 0.4994
## JobRoleSales Representative -3796.217 537.467 -7.063 2.81e-12 ***
## DepartmentResearch & Development -541.770 631.147 -0.858 0.3909
## DepartmentSales -625.344 645.685 -0.968 0.3330
## PerformanceRating4 -273.536 150.438 -1.818 0.0693 .
## WorkLifeBalance2 510.874 260.756 1.959 0.0503 .
## WorkLifeBalance3 463.758 245.145 1.892 0.0588 .
## WorkLifeBalance4 144.179 286.823 0.503 0.6153
## YearsAtCompany 105.031 9.674 10.857 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1841 on 1153 degrees of freedom
## Multiple R-squared: 0.8547, Adjusted R-squared: 0.8526
## F-statistic: 423.7 on 16 and 1153 DF, p-value: < 2.2e-16# now use it to predict the salary
predictMonthlyIncomeResults = predict(linearMod, trainAndAttrit, inverval="predict")
error <- trainAndAttrit$MonthlyIncome - predictMonthlyIncomeResults
rmse = rmse(error)
rmse## [1] 1827.259Use the model to predict with our validation data.
Then take the output of the prior linear regression based approach to predicting the monthlyIncome and write out to seperate files, one file by ID and another file by employee number
#predictMonthIncome
predictMonthlyIncomeResultsValidation = predict(linearMod, predictMonthIncome, inverval="predict")
# create a separate dataset with attrition filled in to avoid future confusion
predictMonthlyIncomeFilledIn = predictMonthIncome
# use the predicted values
predictMonthlyIncomeFilledIn$MonthlyIncome = predictMonthlyIncomeResultsValidation
predictMonthlyIncomeWithID = predictMonthlyIncomeFilledIn[, c("ID", "MonthlyIncome")]
write.csv(predictMonthlyIncomeWithID, file="Case2PredictionsPaugh Salary.csv", row.names=FALSE)
predictMonthlyIncomeWithEmployeeNumber = predictMonthlyIncomeFilledIn[, c("EmployeeNumber", "MonthlyIncome")]
write.csv(predictMonthlyIncomeWithEmployeeNumber, file="Case2PredictionsByENMonthlyIncome.csv", row.names=FALSE)this shows using the linear model with a separate train and test dataset and gives an idea of the resulting confusion matrix results
# Function that returns Root Mean Squared Error
rmse <- function(error)
{
sqrt(mean(error^2))
}
## since this model does NOT use the attrition field, let's include the data for the employees where we are trying to determine attrition too
set.seed(1)
splitPerc = .7
iterations = 500
masterRMSE = matrix(nrow = iterations, 1)
for(j in 1:iterations)
{
trainIndices = sample(1:dim(trainAndAttrit)[1],round(splitPerc * nrow(trainAndAttrit)))
trainData = trainAndAttrit[trainIndices,]
testData = trainAndAttrit[-trainIndices,]
# linear model (after trying many combinations and ruling out variables that failed to improve RMSE and/or had high P values)
linearMod <- lm(trainData$MonthlyIncome~Age + JobRole + Department + PerformanceRating + WorkLifeBalance + YearsAtCompany, data=trainData)
#
summary(linearMod)
# now use it to predict the salary
predictMonthlyIncomeResults = predict(linearMod, testData, inverval="predict")
error <- testData$MonthlyIncome - predictMonthlyIncomeResults
masterRMSE[j,1] = rmse(error)
}
meanRMSE = colMeans(masterRMSE)
meanRMSE## [1] 1868.258let’s try using MASS to automatically find a model for us
This produces very similar results to manually searching for a model. And gets almost the exact same RMSE as the manually
created model above.
# Function that returns Root Mean Squared Error
rmse <- function(error)
{
sqrt(mean(error^2))
}
# remove attrition - tried it initially WITH attrition and the train dataset and it does not use attrition, so removing it here is reasonable
includeForAuto = c(2,4,5,6,7,8,9,12, 13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29.30,31,32,33,34,35,36)
suppressMessages(library(MASS))
# Fit the full model
full.model <- lm(MonthlyIncome ~., data = trainAndAttrit[,includeForAuto])
# Stepwise regression model
step.model <- stepAIC(full.model, direction = "both", trace = FALSE)
summary(step.model)##
## Call:
## lm(formula = MonthlyIncome ~ Age + BusinessTravel + DailyRate +
## Gender + JobInvolvement + JobRole + NumCompaniesWorked +
## OverTime + PerformanceRating + WorkLifeBalance + YearsAtCompany +
## YearsSinceLastPromotion + YearsWithCurrManager, data = trainAndAttrit[,
## includeForAuto])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5511.3 -1210.5 -164.2 1031.4 6865.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3634.5236 481.2996 7.551 8.77e-14 ***
## Age 50.1541 6.9048 7.264 6.95e-13 ***
## BusinessTravelTravel_Frequently 531.8727 204.9612 2.595 0.00958 **
## BusinessTravelTravel_Rarely 367.7609 174.1457 2.112 0.03492 *
## DailyRate 0.2244 0.1329 1.688 0.09171 .
## GenderMale 170.3765 109.4411 1.557 0.11980
## JobInvolvement2 -176.7260 246.1358 -0.718 0.47290
## JobInvolvement3 -404.8020 233.8240 -1.731 0.08368 .
## JobInvolvement4 -476.3376 281.0979 -1.695 0.09043 .
## JobRoleHuman Resources -3286.7355 358.8907 -9.158 < 2e-16 ***
## JobRoleLaboratory Technician -3697.3846 222.2179 -16.639 < 2e-16 ***
## JobRoleManager 8555.3943 280.6907 30.480 < 2e-16 ***
## JobRoleManufacturing Director 27.1115 244.8113 0.111 0.91184
## JobRoleResearch Director 8073.1012 284.8015 28.346 < 2e-16 ***
## JobRoleResearch Scientist -3715.7878 217.8012 -17.060 < 2e-16 ***
## JobRoleSales Executive -372.4237 211.6003 -1.760 0.07867 .
## JobRoleSales Representative -3839.9453 299.9984 -12.800 < 2e-16 ***
## NumCompaniesWorked 91.9624 23.4026 3.930 9.02e-05 ***
## OverTimeYes 167.4848 117.7383 1.423 0.15515
## PerformanceRating4 -248.5127 148.2576 -1.676 0.09397 .
## WorkLifeBalance2 548.6257 257.1397 2.134 0.03309 *
## WorkLifeBalance3 522.5860 241.6950 2.162 0.03081 *
## WorkLifeBalance4 136.4339 282.5710 0.483 0.62931
## YearsAtCompany 120.7798 15.8885 7.602 6.07e-14 ***
## YearsSinceLastPromotion 58.1667 21.0402 2.765 0.00579 **
## YearsWithCurrManager -52.5718 23.5779 -2.230 0.02596 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1811 on 1144 degrees of freedom
## Multiple R-squared: 0.8604, Adjusted R-squared: 0.8573
## F-statistic: 281.9 on 25 and 1144 DF, p-value: < 2.2e-16predictMonthlyIncomeResultsAIC = predict(linearMod, trainAndAttrit, inverval="predict")
error <- trainAndAttrit$MonthlyIncome - predictMonthlyIncomeResultsAIC
rmse = rmse(error)
rmse## [1] 1831.646let’s try a random forest for predicting attrition
This generates an RMSE
library(randomForest)
suppressMessages(library(e1071))
suppressMessages(library(caret))
rmse <- function(error)
{
sqrt(mean(error^2))
}
set.seed(71)
includeForRF = c(2,3,4,5,6,7,8,9,12, 13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29.30,31,32,33,34,35,36)
rfModel <- randomForest(Attrition ~ ., data=train[,includeForRF], ntree=500, importance=TRUE,
proximity=TRUE)
summary(rfModel)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 870 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1740 matrix numeric
## oob.times 870 -none- numeric
## classes 2 -none- character
## importance 120 -none- numeric
## importanceSD 90 -none- numeric
## localImportance 0 -none- NULL
## proximity 756900 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 870 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callpredictAttritionRF = predict(rfModel, train)
CM = confusionMatrix(table(predictAttritionRF,train$Attrition),positive="Yes")
CM## Confusion Matrix and Statistics
##
##
## predictAttritionRF No Yes
## No 730 0
## Yes 0 140
##
## Accuracy : 1
## 95% CI : (0.9958, 1)
## No Information Rate : 0.8391
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.1609
## Detection Rate : 0.1609
## Detection Prevalence : 0.1609
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : Yes
## Show Random Forest Confusion Matrix
# write out the naive bayes results
draw_confusion_matrix <- function(cmtrx) {
total <- sum(cmtrx$table)
res <- as.numeric(cmtrx$table)
# Generate color gradients. Palettes come from RColorBrewer.
greenPalette <- c("#F7FCF5","#E5F5E0","#C7E9C0","#A1D99B","#74C476","#41AB5D","#238B45","#006D2C","#00441B")
redPalette <- c("#FFF5F0","#FEE0D2","#FCBBA1","#FC9272","#FB6A4A","#EF3B2C","#CB181D","#A50F15","#67000D")
getColor <- function (greenOrRed = "green", amount = 0) {
if (amount == 0)
return("#FFFFFF")
palette <- greenPalette
if (greenOrRed == "red")
palette <- redPalette
colorRampPalette(palette)(100)[10 + ceiling(90 * amount / total)]
}
# set the basic layout
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX - Training Data', cex.main=2)
# create the matrix
classes = colnames(cmtrx$table)
rect(150, 430, 240, 370, col=getColor("green", res[1]))
text(195, 435, classes[1], cex=1.2)
rect(250, 430, 340, 370, col=getColor("red", res[3]))
text(295, 435, classes[2], cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col=getColor("red", res[2]))
rect(250, 305, 340, 365, col=getColor("green", res[4]))
text(140, 400, classes[1], cex=1.2, srt=90)
text(140, 335, classes[2], cex=1.2, srt=90)
# add in the cmtrx results
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cmtrx$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cmtrx$byClass[1]), 3), cex=1.2)
text(30, 85, names(cmtrx$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cmtrx$byClass[2]), 3), cex=1.2)
text(50, 85, names(cmtrx$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cmtrx$byClass[5]), 3), cex=1.2)
text(70, 85, names(cmtrx$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cmtrx$byClass[6]), 3), cex=1.2)
text(90, 85, names(cmtrx$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cmtrx$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cmtrx$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cmtrx$overall[1]), 3), cex=1.4)
text(70, 35, names(cmtrx$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cmtrx$overall[2]), 3), cex=1.4)
}
draw_confusion_matrix(CM)
#let’s try using randem forest for attrition, but this time let’s try varying the # of trees to see what effect this has. ## The results show that the # of trees really doesn’t change things
set.seed(1)
splitPerc = .7
iterations = 50
masterAcc = matrix(nrow = iterations, ncol = 1)
masterSpec = matrix(nrow = iterations, ncol = 1)
masterSens = matrix(nrow = iterations, ncol = 1)
for(j in 1:iterations)
{
trainIndices = sample(1:dim(train)[1],round(splitPerc * nrow(train)))
trainData = train[trainIndices,]
testData = train[-trainIndices,]
#classifications = knn(scale(trainData[,c(3,4)]),scale(testData[,c(3,4)]),as.factor(trainData$Attrition), prob = TRUE, k = i)
rfModelTest = randomForest(Attrition ~ ., data=trainData[,includeForRF], ntree=500, importance=TRUE,
proximity=TRUE)
predictAttritionRF = predict(rfModelTest, newdata=testData)
CM = confusionMatrix(table(predictAttritionRF, as.factor(testData$Attrition)),positive="Yes")
masterAcc[j,1] = CM$overall[1]
masterSens[j,1] = CM$byClass[1][[1]] # sensitivity
masterSpec[j,1] = CM$byClass[2][[1]] # specificity
CM
}
MeanAcc = colMeans(masterAcc)
MeanAcc## [1] 0.8564751MeanSens = colMeans(masterSens)
MeanSens## [1] 0.1577432MeanSpec = colMeans(masterSpec)
MeanSpec## [1] 0.9913149CM## Confusion Matrix and Statistics
##
##
## predictAttritionRF No Yes
## No 219 36
## Yes 0 6
##
## Accuracy : 0.8621
## 95% CI : (0.8142, 0.9015)
## No Information Rate : 0.8391
## P-Value [Acc > NIR] : 0.1777
##
## Kappa : 0.2186
##
## Mcnemar's Test P-Value : 5.433e-09
##
## Sensitivity : 0.14286
## Specificity : 1.00000
## Pos Pred Value : 1.00000
## Neg Pred Value : 0.85882
## Prevalence : 0.16092
## Detection Rate : 0.02299
## Detection Prevalence : 0.02299
## Balanced Accuracy : 0.57143
##
## 'Positive' Class : Yes
## the specificity is very low still on the Random Forest and Naive Bayes results. Can we improve this using Logistic regression?
based on the ROC curve, a threshold of 0.15 or so gives very good sensitivity and specificity
suppressMessages(library(ROCR))
# use the value with the monthly income filled in! Use our calculated values - they may be useful for predicting attrition
trainAndMonthly = rbind(train, predictMonthlyIncomeFilledIn)
includeForLogReg = c(2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29.30,31,32,33,34,35,36)
logRegModel <- glm(Attrition ~ ., data=trainAndMonthly[includeForLogReg], family = "binomial") %>%
stepAIC(trace = FALSE)
summary(logRegModel)##
## Call:
## glm(formula = Attrition ~ Age + BusinessTravel + DailyRate +
## DistanceFromHome + EnvironmentSatisfaction + Gender + HourlyRate +
## JobInvolvement + JobRole + JobSatisfaction + NumCompaniesWorked +
## OverTime + RelationshipSatisfaction + StockOptionLevel +
## TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany +
## YearsInCurrentRole + YearsSinceLastPromotion + YearsWithCurrManager,
## family = "binomial", data = trainAndMonthly[includeForLogReg])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0204 -0.4603 -0.2361 -0.0789 3.5993
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.1902322 1.0551366 2.076 0.037914 *
## Age -0.0503305 0.0136027 -3.700 0.000216 ***
## BusinessTravelTravel_Frequently 1.7102206 0.4518799 3.785 0.000154 ***
## BusinessTravelTravel_Rarely 0.7855449 0.4144892 1.895 0.058064 .
## DailyRate -0.0004776 0.0002538 -1.882 0.059825 .
## DistanceFromHome 0.0514651 0.0125252 4.109 3.97e-05 ***
## EnvironmentSatisfaction2 -1.2836189 0.3250667 -3.949 7.85e-05 ***
## EnvironmentSatisfaction3 -1.0993517 0.2874468 -3.825 0.000131 ***
## EnvironmentSatisfaction4 -1.2663205 0.2863953 -4.422 9.80e-06 ***
## GenderMale 0.4107057 0.2101575 1.954 0.050669 .
## HourlyRate 0.0080851 0.0050779 1.592 0.111336
## JobInvolvement2 -1.3521622 0.4086767 -3.309 0.000938 ***
## JobInvolvement3 -1.6805300 0.3844921 -4.371 1.24e-05 ***
## JobInvolvement4 -2.3565936 0.5393922 -4.369 1.25e-05 ***
## JobRoleHuman Resources 1.4051608 0.6426577 2.186 0.028780 *
## JobRoleLaboratory Technician 1.4558831 0.5062432 2.876 0.004029 **
## JobRoleManager 0.2050522 0.7090179 0.289 0.772424
## JobRoleManufacturing Director 0.3681330 0.6177437 0.596 0.551221
## JobRoleResearch Director -1.7350275 1.0297330 -1.685 0.092002 .
## JobRoleResearch Scientist 0.6385064 0.5030905 1.269 0.204381
## JobRoleSales Executive 0.9987956 0.4940999 2.021 0.043234 *
## JobRoleSales Representative 2.5429493 0.5679789 4.477 7.56e-06 ***
## JobSatisfaction2 -0.4983214 0.3109363 -1.603 0.109012
## JobSatisfaction3 -0.4834701 0.2754616 -1.755 0.079238 .
## JobSatisfaction4 -1.2480446 0.2978066 -4.191 2.78e-05 ***
## NumCompaniesWorked 0.1915761 0.0417684 4.587 4.50e-06 ***
## OverTimeYes 2.1405217 0.2247395 9.524 < 2e-16 ***
## RelationshipSatisfaction2 -0.9113317 0.3218965 -2.831 0.004638 **
## RelationshipSatisfaction3 -0.9701898 0.2944381 -3.295 0.000984 ***
## RelationshipSatisfaction4 -0.9469180 0.2868074 -3.302 0.000961 ***
## StockOptionLevel1 -1.2487758 0.2300506 -5.428 5.69e-08 ***
## StockOptionLevel2 -1.9674147 0.4745826 -4.146 3.39e-05 ***
## StockOptionLevel3 -0.5604686 0.4160425 -1.347 0.177934
## TrainingTimesLastYear -0.1573023 0.0848567 -1.854 0.063776 .
## WorkLifeBalance2 -0.9962560 0.4124808 -2.415 0.015723 *
## WorkLifeBalance3 -1.3570905 0.3859535 -3.516 0.000438 ***
## WorkLifeBalance4 -1.1072712 0.4798379 -2.308 0.021022 *
## YearsAtCompany 0.0812365 0.0379904 2.138 0.032489 *
## YearsInCurrentRole -0.1420790 0.0516462 -2.751 0.005941 **
## YearsSinceLastPromotion 0.1451945 0.0492241 2.950 0.003181 **
## YearsWithCurrManager -0.1757007 0.0529565 -3.318 0.000907 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1041.34 on 1169 degrees of freedom
## Residual deviance: 660.19 on 1129 degrees of freedom
## AIC: 742.19
##
## Number of Fisher Scoring iterations: 7predictAttrLogisticRegresionProbs = predict(logRegModel, trainAndMonthly, inverval="predict", type = "response")
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > 0.2, "Yes", "No")
predictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
length(predictAttrLogisticRegresion)## [1] 1170CM = confusionMatrix(table(predictAttrLogisticRegresion,trainAndMonthly$Attrition), positive="Yes")
CM## Confusion Matrix and Statistics
##
##
## predictAttrLogisticRegresion No Yes
## No 822 43
## Yes 157 148
##
## Accuracy : 0.8291
## 95% CI : (0.8062, 0.8502)
## No Information Rate : 0.8368
## P-Value [Acc > NIR] : 0.775
##
## Kappa : 0.4955
##
## Mcnemar's Test P-Value : 1.346e-15
##
## Sensitivity : 0.7749
## Specificity : 0.8396
## Pos Pred Value : 0.4852
## Neg Pred Value : 0.9503
## Prevalence : 0.1632
## Detection Rate : 0.1265
## Detection Prevalence : 0.2607
## Balanced Accuracy : 0.8073
##
## 'Positive' Class : Yes
## # let's lot the ROC curve
ROCRPred <- prediction(predictAttrLogisticRegresionProbs, trainAndMonthly$Attrition)
ROCRPerf <- performance(ROCRPred,"tpr","fpr")
plot(ROCRPerf,colorize = TRUE, print.cutoffs.at = seq(0.1, by = 0.1))
Show AIC Linear regression Confision Matrix
# write out the naive bayes results
draw_confusion_matrix <- function(cmtrx) {
total <- sum(cmtrx$table)
res <- as.numeric(cmtrx$table)
# Generate color gradients. Palettes come from RColorBrewer.
greenPalette <- c("#F7FCF5","#E5F5E0","#C7E9C0","#A1D99B","#74C476","#41AB5D","#238B45","#006D2C","#00441B")
redPalette <- c("#FFF5F0","#FEE0D2","#FCBBA1","#FC9272","#FB6A4A","#EF3B2C","#CB181D","#A50F15","#67000D")
getColor <- function (greenOrRed = "green", amount = 0) {
if (amount == 0)
return("#FFFFFF")
palette <- greenPalette
if (greenOrRed == "red")
palette <- redPalette
colorRampPalette(palette)(100)[10 + ceiling(90 * amount / total)]
}
# set the basic layout
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX - Training Data', cex.main=2)
# create the matrix
classes = colnames(cmtrx$table)
rect(150, 430, 240, 370, col=getColor("green", res[1]))
text(195, 435, classes[1], cex=1.2)
rect(250, 430, 340, 370, col=getColor("red", res[3]))
text(295, 435, classes[2], cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col=getColor("red", res[2]))
rect(250, 305, 340, 365, col=getColor("green", res[4]))
text(140, 400, classes[1], cex=1.2, srt=90)
text(140, 335, classes[2], cex=1.2, srt=90)
# add in the cmtrx results
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cmtrx$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cmtrx$byClass[1]), 3), cex=1.2)
text(30, 85, names(cmtrx$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cmtrx$byClass[2]), 3), cex=1.2)
text(50, 85, names(cmtrx$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cmtrx$byClass[5]), 3), cex=1.2)
text(70, 85, names(cmtrx$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cmtrx$byClass[6]), 3), cex=1.2)
text(90, 85, names(cmtrx$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cmtrx$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cmtrx$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cmtrx$overall[1]), 3), cex=1.4)
text(70, 35, names(cmtrx$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cmtrx$overall[2]), 3), cex=1.4)
}
draw_confusion_matrix(CM)
let’s try that last model against our validation data, with prob > 0.20
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > 0.15, "Yes", "No")
head(train$Attrition)## [1] No No No No No No
## Levels: No YespredictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
length(predictAttrLogisticRegresion)## [1] 1170str(predictAttrLogisticRegresion)## Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 1 2 1 ...
## - attr(*, "names")= chr [1:1170] "1" "2" "3" "4" ...str(train$Attrition)## Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...head(predictAttrLogisticRegresion)## 1 2 3 4 5 6
## No No No No Yes No
## Levels: No YesCM = confusionMatrix(table(predictAttrLogisticRegresion,trainAndMonthly$Attrition),positive="Yes")
CM## Confusion Matrix and Statistics
##
##
## predictAttrLogisticRegresion No Yes
## No 764 34
## Yes 215 157
##
## Accuracy : 0.7872
## 95% CI : (0.7626, 0.8103)
## No Information Rate : 0.8368
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.4361
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.8220
## Specificity : 0.7804
## Pos Pred Value : 0.4220
## Neg Pred Value : 0.9574
## Prevalence : 0.1632
## Detection Rate : 0.1342
## Detection Prevalence : 0.3179
## Balanced Accuracy : 0.8012
##
## 'Positive' Class : Yes
## try the GLM model with train/test split to gauge the confusion matrix results
this is terribly slow and it gets a different model each time… so very difficult to gauge effectiveness this way
This is way too slow, and the model is different every time, so what’s the point?
We want to train and test with a consistent model across all our cross-validation steps
set.seed(1)
splitPerc = .7
iterations = 2
masterAcc = matrix(nrow = iterations, 1)
masterSpec = matrix(nrow = iterations, 1)
masterSens = matrix(nrow = iterations, 1)
for(j in 1:iterations)
{
trainIndices = sample(1:dim(trainAndMonthly)[1],round(splitPerc * nrow(trainAndMonthly)))
trainData = trainAndMonthly[trainIndices,]
testData = trainAndMonthly[-trainIndices,]
logRegModel <- glm(Attrition ~ ., data=trainAndMonthly[includeForLogReg], family = "binomial") %>%
stepAIC(trace = FALSE)
predictAttrLogisticRegresionProbs = predict(logRegModel, trainAndMonthly, inverval="predict", type = "response")
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > 0.2, "Yes", "No")
predictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
length(predictAttrLogisticRegresion)
CM = confusionMatrix(table(predictAttrLogisticRegresion,trainAndMonthly$Attrition),positive="Yes")
CM
masterAcc[j,1] = CM$overall[1]
masterSens[j,1] = CM$byClass[1][[1]] # sensitivity
masterSpec[j,1] = CM$byClass[2][[1]] # specificity
}
masterAcc## [,1]
## [1,] 0.8290598
## [2,] 0.8290598MeanAcc = colMeans(masterAcc)
MeanAcc## [1] 0.8290598using a manual model with the most promising fields
create the model with the training data
suppressMessages(library(ROCR))
# use the value with the monthly income filled in! Use our calculated values - they may be useful for predicting attrition
trainAndMonthly = rbind(train, predictMonthlyIncomeFilledIn)
includeForLogReg = c(2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29.30,31,32,33,34,35,36)
logRegModel <- glm(Attrition ~ Age + BusinessTravel + DailyRate + DistanceFromHome + EnvironmentSatisfaction + HourlyRate + JobLevel + JobRole + JobSatisfaction + NumCompaniesWorked + OverTime + StockOptionLevel + WorkLifeBalance + YearsAtCompany + YearsSinceLastPromotion, data=trainAndMonthly[includeForLogReg], family = "binomial")
summary(logRegModel)##
## Call:
## glm(formula = Attrition ~ Age + BusinessTravel + DailyRate +
## DistanceFromHome + EnvironmentSatisfaction + HourlyRate +
## JobLevel + JobRole + JobSatisfaction + NumCompaniesWorked +
## OverTime + StockOptionLevel + WorkLifeBalance + YearsAtCompany +
## YearsSinceLastPromotion, family = "binomial", data = trainAndMonthly[includeForLogReg])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9741 -0.4887 -0.2711 -0.1123 3.9241
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.2552671 0.9486894 1.323 0.185782
## Age -0.0441123 0.0130892 -3.370 0.000751 ***
## BusinessTravelTravel_Frequently 1.5161498 0.4310237 3.518 0.000436 ***
## BusinessTravelTravel_Rarely 0.7032475 0.3978659 1.768 0.077136 .
## DailyRate -0.0005161 0.0002456 -2.102 0.035587 *
## DistanceFromHome 0.0440787 0.0118485 3.720 0.000199 ***
## EnvironmentSatisfaction2 -1.1946833 0.3109987 -3.841 0.000122 ***
## EnvironmentSatisfaction3 -1.1231583 0.2744110 -4.093 4.26e-05 ***
## EnvironmentSatisfaction4 -1.2057602 0.2729599 -4.417 9.99e-06 ***
## HourlyRate 0.0071089 0.0048619 1.462 0.143695
## JobLevel2 -1.3520656 0.3971628 -3.404 0.000663 ***
## JobLevel3 -1.6854379 0.3750157 -4.494 6.98e-06 ***
## JobLevel4 -2.2746771 0.5221527 -4.356 1.32e-05 ***
## JobRoleHuman Resources 1.2625238 0.6136876 2.057 0.039660 *
## JobRoleLaboratory Technician 1.2358337 0.4812186 2.568 0.010225 *
## JobRoleManager 0.2396844 0.6894364 0.348 0.728101
## JobRoleManufacturing Director 0.0929140 0.5873320 0.158 0.874302
## JobRoleResearch Director -1.4759585 0.9287324 -1.589 0.112011
## JobRoleResearch Scientist 0.5947935 0.4796452 1.240 0.214950
## JobRoleSales Executive 0.8427604 0.4707331 1.790 0.073403 .
## JobRoleSales Representative 2.4020655 0.5393920 4.453 8.46e-06 ***
## JobSatisfaction2 -0.3854666 0.2983800 -1.292 0.196404
## JobSatisfaction3 -0.4208734 0.2624351 -1.604 0.108775
## JobSatisfaction4 -1.1503510 0.2842737 -4.047 5.20e-05 ***
## NumCompaniesWorked 0.1690484 0.0398307 4.244 2.19e-05 ***
## OverTimeYes 1.9865547 0.2114434 9.395 < 2e-16 ***
## StockOptionLevel1 -1.1332201 0.2197640 -5.157 2.52e-07 ***
## StockOptionLevel2 -1.8406855 0.4562480 -4.034 5.47e-05 ***
## StockOptionLevel3 -0.4460563 0.4017835 -1.110 0.266917
## WorkLifeBalance2 -1.0772779 0.3928362 -2.742 0.006101 **
## WorkLifeBalance3 -1.3618824 0.3648384 -3.733 0.000189 ***
## WorkLifeBalance4 -1.2042954 0.4599882 -2.618 0.008842 **
## YearsAtCompany -0.0523385 0.0271194 -1.930 0.053616 .
## YearsSinceLastPromotion 0.1060962 0.0448531 2.365 0.018010 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1041.3 on 1169 degrees of freedom
## Residual deviance: 703.5 on 1136 degrees of freedom
## AIC: 771.5
##
## Number of Fisher Scoring iterations: 6predictAttrLogisticRegresionProbs = predict(logRegModel, trainAndMonthly, inverval="predict", type = "response")
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > 0.15, "Yes", "No")
predictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
length(predictAttrLogisticRegresion)## [1] 1170CM = confusionMatrix(table(predictAttrLogisticRegresion,trainAndMonthly$Attrition),positive="Yes")
CM## Confusion Matrix and Statistics
##
##
## predictAttrLogisticRegresion No Yes
## No 743 39
## Yes 236 152
##
## Accuracy : 0.765
## 95% CI : (0.7396, 0.789)
## No Information Rate : 0.8368
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.392
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.7958
## Specificity : 0.7589
## Pos Pred Value : 0.3918
## Neg Pred Value : 0.9501
## Prevalence : 0.1632
## Detection Rate : 0.1299
## Detection Prevalence : 0.3316
## Balanced Accuracy : 0.7774
##
## 'Positive' Class : Yes
## # let's lot the ROC curve
ROCRPred <- prediction(predictAttrLogisticRegresionProbs, trainAndMonthly$Attrition)
ROCRPerf <- performance(ROCRPred,"tpr","fpr")
plot(ROCRPerf,colorize = TRUE, print.cutoffs.at = seq(0.1, by = 0.1))
using a very low threshold here to make sure the specificity is higher than our 0.6 goal, even if it results in lower accuracy.
predictAttritionResultsValidationProbs = predict(logRegModel, predictAttr, inverval="predict", type = "response")
predictAttritionResultsValidation <- ifelse(predictAttritionResultsValidationProbs > 0.12, "Yes", "No")
# create a separate dataset with attrition filled in to avoid future confusion
predictAttrFilledIn = predictAttr
# use the predicted values
predictAttrFilledIn$Attrition = predictAttritionResultsValidation
predictAttrWithID = predictAttrFilledIn[, c("ID", "Attrition")]
write.csv(predictAttrWithID, file="Case2PredictionsPaugh Attrition.csv", row.names=FALSE)
predictAttrWithEmployeeNumber = predictAttrFilledIn[, c("EmployeeNumber", "Attrition")]
write.csv(predictAttrWithEmployeeNumber, file="Case2PredictionsByENAttrition.csv", row.names=FALSE)using a manual model with the most promosing fields
This creates essentially a manual ROC curve of the cross validation data, by calculating the specificity and sensitivity at each point
set.seed(1)
splitPerc = .7
iterations = 50
thresholdValues = seq(0,1,0.01)
thresholdIterations = length(thresholdValues)
masterAcc = matrix(nrow = iterations, ncol=thresholdIterations)
masterSpec = matrix(nrow = iterations, ncol=thresholdIterations)
masterSens = matrix(nrow = iterations, ncol=thresholdIterations)
masterCombined = matrix(nrow = iterations, ncol=thresholdIterations)
for(j in 1:iterations)
{
trainIndices = sample(1:dim(trainAndMonthly)[1],round(splitPerc * nrow(trainAndMonthly)))
trainData = trainAndMonthly[trainIndices,]
testData = trainAndMonthly[-trainIndices,]
logRegModel <- glm(Attrition ~ Age + BusinessTravel + DailyRate + DistanceFromHome + Education + EnvironmentSatisfaction + HourlyRate + JobLevel + JobRole + JobSatisfaction + NumCompaniesWorked + OverTime + StockOptionLevel + WorkLifeBalance + YearsAtCompany + YearsSinceLastPromotion, data=trainData, family = "binomial")
predictAttrLogisticRegresionProbs = predict(logRegModel, testData, inverval="predict", type = "response")
for (i in 1:length(thresholdValues))
{
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > thresholdValues[i], "Yes", "No")
predictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
levels(predictAttrLogisticRegresion) = levels(testData$Attrition)
CM = confusionMatrix(table(predictAttrLogisticRegresion, as.factor(testData$Attrition)),positive="Yes")
masterAcc[j,i] = CM$overall[1]
masterSens[j,i] = CM$byClass[1][[1]] # sensitivity
masterSpec[j,i] = CM$byClass[2][[1]] # specificity
masterCombined[j,i] = CM$byClass[1][[1]] + CM$byClass[2][[1]] # combined
}
}
# all the rows in the same column have the same threshold value, so average across the column to get the average for threshold
MeanAcc = colMeans(masterAcc)
plot(seq(1,thresholdIterations,1),MeanAcc, type = "l", main="Accuracy by Threshold",xlab = "Threshold", ylab="Accuracy")
which.max(MeanAcc)## [1] 58max(MeanAcc)## [1] 0.8727066MeanSens = colMeans(masterSens)
plot(seq(1,thresholdIterations,1),MeanSens, type = "l", main="Sensitivity by Threshold", xlab = "Threshold", ylab="Sensitivity")
which.max(MeanSens)## [1] 2thresholdValues[which.max(MeanSens)]## [1] 0.01max(MeanSens)## [1] 0.9624236MeanSpec = colMeans(masterSpec)
plot(seq(1,thresholdIterations,1),MeanSpec, type = "l", main="Specificity by Threshold",xlab = "Threshold", ylab="Specificity")
which.max(MeanSpec)## [1] 1thresholdValues[which.max(MeanSpec)]## [1] 0max(MeanSpec)## [1] 1MeanCombined = colMeans(masterCombined)
plot(seq(1,thresholdIterations,1),MeanCombined, type = "l", main="Sensitivity + Specificity by Threshold", xlab = "Threshold", ylab="Sensitivity + Specificity")
which.max(MeanCombined)## [1] 22thresholdValues[which.max(MeanCombined)]## [1] 0.21max(MeanCombined)## [1] 1.513913dfTestingResults = data.frame(MeanCombined)
names(dfTestingResults)[1]<-"Sum"
dfTestingResults$Threshold =thresholdValues
dfTestingResults$Specificity = MeanSpec
dfTestingResults$Sensitivity = MeanSens
#dfTestingResults
# this works but it does not show a group name
p = ggplot() +
geom_line(data = dfTestingResults, aes(x = Threshold, y = MeanSpec), color = "blue") +
geom_line(data = dfTestingResults, aes(x = Threshold, y = MeanSens), color = "red") +
geom_line(data = dfTestingResults, aes(x = Threshold, y = Sum), color = "green") +
xlab('Dates') +
ylab('percent.change') + theme_economist()
p
dfMeltThresholdData <- melt(dfTestingResults, id.vars="Threshold", value.name="value")
ggplot(dfMeltThresholdData, aes(x=Threshold, y=value, color=variable)) + ylab("Value") + ggtitle("Test Results by Threshold") +
geom_point()
lets try logistic regression but with a manually created model
this uses the new variables that focus on the factors that led to attrtion in our training data
#str(trainAndMonthly)
trainAndMonthly = rbind(train, predictMonthlyIncomeFilledIn)
includeForLogReg = c(2,3,4,5,6,7,8,9,12, 13,14,15,16,17,18,19,21,22,24,25,26,27,28,29.30,31,32,33,34,35,36)
#logRegModel <- glm(Attrition ~ BusinessTravelFrequently + LowEducation + LowEnvironmentalSatisfaction + LowJobLevel + JobRoleSales + HighCompaniesWorked + LowWorkLifeBalance + JobRoleMed + Age + YearsInCurrentRole + DistanceFromHome + OverTime + DailyRate, data=trainAndMonthly, family = "binomial") %>%
# stepAIC(trace = FALSE)
logRegModel <- glm(Attrition ~ BusinessTravelFrequently + LowEducation + LowEnvironmentalSatisfaction + LowJobLevel + JobRoleSales + HighCompaniesWorked + LowWorkLifeBalance + JobRoleMed + Age + DistanceFromHome + OverTime, data=trainAndMonthly, family = "binomial")
summary(logRegModel)##
## Call:
## glm(formula = Attrition ~ BusinessTravelFrequently + LowEducation +
## LowEnvironmentalSatisfaction + LowJobLevel + JobRoleSales +
## HighCompaniesWorked + LowWorkLifeBalance + JobRoleMed + Age +
## DistanceFromHome + OverTime, family = "binomial", data = trainAndMonthly)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7015 -0.5492 -0.3756 -0.2110 3.0714
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.54587 0.80554 -4.402 1.07e-05 ***
## BusinessTravelFrequentlyYes 0.73073 0.20739 3.523 0.000426 ***
## LowEducationYes 0.31258 0.58445 0.535 0.592763
## LowEnvironmentalSatisfactionYes 0.39386 0.20083 1.961 0.049860 *
## LowJobLevelYes 0.60182 0.21488 2.801 0.005099 **
## JobRoleSalesYes 1.84317 0.40670 4.532 5.84e-06 ***
## HighCompaniesWorkedYes 1.01589 0.22425 4.530 5.90e-06 ***
## LowWorkLifeBalanceYes 1.16410 0.31436 3.703 0.000213 ***
## JobRoleMedYes 0.75751 0.27752 2.730 0.006342 **
## Age -0.03077 0.01137 -2.706 0.006802 **
## DistanceFromHome 0.03758 0.01081 3.475 0.000512 ***
## OverTimeYes 1.62067 0.18213 8.899 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1041.3 on 1169 degrees of freedom
## Residual deviance: 832.9 on 1158 degrees of freedom
## AIC: 856.9
##
## Number of Fisher Scoring iterations: 5predictAttrLogisticRegresionProbs = predict(logRegModel, trainAndMonthly, inverval="predict", type = "response")
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > 0.2, "Yes", "No")
head(train$Attrition)## [1] No No No No No No
## Levels: No YespredictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
length(predictAttrLogisticRegresion)## [1] 1170str(predictAttrLogisticRegresion)## Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 1 2 1 ...
## - attr(*, "names")= chr [1:1170] "1" "2" "3" "4" ...str(train$Attrition)## Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...head(predictAttrLogisticRegresion)## 1 2 3 4 5 6
## No No No No Yes No
## Levels: No YesCM = confusionMatrix(table(predictAttrLogisticRegresion,trainAndMonthly$Attrition),positive="Yes")
CM## Confusion Matrix and Statistics
##
##
## predictAttrLogisticRegresion No Yes
## No 770 63
## Yes 209 128
##
## Accuracy : 0.7675
## 95% CI : (0.7422, 0.7915)
## No Information Rate : 0.8368
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.3492
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.6702
## Specificity : 0.7865
## Pos Pred Value : 0.3798
## Neg Pred Value : 0.9244
## Prevalence : 0.1632
## Detection Rate : 0.1094
## Detection Prevalence : 0.2880
## Balanced Accuracy : 0.7283
##
## 'Positive' Class : Yes
## # let's lot the ROC curve
ROCRPred <- prediction(predictAttrLogisticRegresionProbs, trainAndMonthly$Attrition)
ROCRPerf <- performance(ROCRPred,"tpr","fpr")
plot(ROCRPerf,colorize = TRUE, print.cutoffs.at = seq(0.1, by = 0.1))
write out to file for the alternative model
predictAttritionResultsValidationProbs = predict(logRegModel, predictAttr, inverval="predict", type = "response")
predictAttritionResultsValidation <- ifelse(predictAttritionResultsValidationProbs > 0.12, "Yes", "No")
# create a separate dataset with attrition filled in to avoid future confusion
predictAttrFilledIn = predictAttr
# use the predicted values
predictAttrFilledIn$Attrition = predictAttritionResultsValidation
predictAttrWithID = predictAttrFilledIn[, c("ID", "Attrition")]
write.csv(predictAttrWithID, file="Case2PredictionsPaugh Attrition.csv", row.names=FALSE)
predictAttrWithEmployeeNumber = predictAttrFilledIn[, c("EmployeeNumber", "Attrition")]
write.csv(predictAttrWithEmployeeNumber, file="Case2PredictionsByENAttrition.csv", row.names=FALSE)repeat manual model, but not with many iteration of train/test testing to check confusion matrix results
set.seed(1)
splitPerc = .7
iterations = 50
thresholdValues = seq(0,1,0.01)
thresholdIterations = length(thresholdValues)
masterAcc = matrix(nrow = iterations, ncol=thresholdIterations)
masterSpec = matrix(nrow = iterations, ncol=thresholdIterations)
masterSens = matrix(nrow = iterations, ncol=thresholdIterations)
masterCombined = matrix(nrow = iterations, ncol=thresholdIterations)
for(j in 1:iterations)
{
trainIndices = sample(1:dim(trainAndMonthly)[1],round(splitPerc * nrow(trainAndMonthly)))
trainData = trainAndMonthly[trainIndices,]
testData = trainAndMonthly[-trainIndices,]
#logRegModel <- glm(Attrition ~ BusinessTravelFrequently + LowEducation + LowEnvironmentalSatisfaction + LowJobLevel + JobRoleSales + HighCompaniesWorked + LowWorkLifeBalance + JobRoleMed + Age + YearsInCurrentRole + DistanceFromHome + OverTime + DailyRate, data=trainAndMonthly, family = "binomial") %>%
# stepAIC(trace = FALSE)
logRegModel <- glm(Attrition ~ BusinessTravelFrequently + LowEducation + LowEnvironmentalSatisfaction + LowJobLevel + JobRoleSales + HighCompaniesWorked + LowWorkLifeBalance + JobRoleMed + Age + DistanceFromHome + OverTime, data=trainData, family = "binomial")
predictAttrLogisticRegresionProbs = predict(logRegModel, testData, inverval="predict", type = "response")
for (i in 1:length(thresholdValues))
{
predictAttrLogisticRegresion <- ifelse(predictAttrLogisticRegresionProbs > thresholdValues[i], "Yes", "No")
predictAttrLogisticRegresion <- as.factor(predictAttrLogisticRegresion)
levels(predictAttrLogisticRegresion) = levels(testData$Attrition)
CM = confusionMatrix(table(predictAttrLogisticRegresion, as.factor(testData$Attrition)),positive="Yes")
masterAcc[j,i] = CM$overall[1]
masterSens[j,i] = CM$byClass[1][[1]] # sensitivity
masterSpec[j,i] = CM$byClass[2][[1]] # specificity
masterCombined[j,i] = CM$byClass[1][[1]] + CM$byClass[2][[1]] # combined
}
}
# all the rows in the same column have the same threshold value, so average across the column to get the average for threshold
MeanAcc = colMeans(masterAcc)
plot(seq(1,thresholdIterations,1),MeanAcc, type = "l", xlab = "Threshold", ylab="Accuracy")
which.max(MeanAcc)## [1] 56max(MeanAcc)## [1] 0.8500855MeanSens = colMeans(masterSens)
plot(seq(1,thresholdIterations,1),MeanSens, type = "l", xlab = "Threshold", ylab="Sensitivity")
which.max(MeanSens)## [1] 3thresholdValues[which.max(MeanSens)]## [1] 0.02max(MeanSens)## [1] 0.9727179MeanSpec = colMeans(masterSpec)
plot(seq(1,thresholdIterations,1),MeanSpec, type = "l", xlab = "Threshold", ylab="Specificity")
which.max(MeanSpec)## [1] 1thresholdValues[which.max(MeanSpec)]## [1] 0max(MeanSpec)## [1] 1MeanCombined = colMeans(masterCombined)
plot(seq(1,thresholdIterations,1),MeanCombined, type = "l", xlab = "Threshold", ylab="Sensitivity + Specificity")
which.max(MeanCombined)## [1] 20thresholdValues[which.max(MeanCombined)]## [1] 0.19max(MeanCombined)## [1] 1.439611dfTestingResults <- data.frame(MeanCombined)
dfTestingResults <- dfTestingResults %>% rename( Sum = MeanCombined )
dfTestingResults$Threshold =thresholdValues
dfTestingResults$Specificity = MeanSpec
dfTestingResults$Sensitivity = MeanSens
dfTestingResults## Sum Threshold Specificity Sensitivity
## 1 1.000000 0.00 1.00000000 0.0000000000
## 2 1.011088 0.01 0.15907729 0.8520111802
## 3 1.068507 0.02 0.09578871 0.9727178944
## 4 1.118129 0.03 0.17192699 0.9462019650
## 5 1.162486 0.04 0.24435949 0.9181263242
## 6 1.212290 0.05 0.30835999 0.9039304456
## 7 1.260490 0.06 0.36909701 0.8913933031
## 8 1.301919 0.07 0.42408913 0.8778296026
## 9 1.341031 0.08 0.48003504 0.8609959012
## 10 1.364659 0.09 0.52409935 0.8405596611
## 11 1.386943 0.10 0.56633907 0.8206040243
## 12 1.402382 0.11 0.60426624 0.7981160844
## 13 1.412552 0.12 0.63835865 0.7741935757
## 14 1.419301 0.13 0.66625676 0.7530440878
## 15 1.425738 0.14 0.69165592 0.7340823020
## 16 1.433053 0.15 0.71366805 0.7193852186
## 17 1.432908 0.16 0.73173313 0.7011751111
## 18 1.436831 0.17 0.74907238 0.6877587381
## 19 1.436696 0.18 0.76444701 0.6722493551
## 20 1.439611 0.19 0.77892351 0.6606878852
## 21 1.438783 0.20 0.79160895 0.6471737689
## 22 1.437386 0.21 0.80535638 0.6320295105
## 23 1.437974 0.22 0.81870998 0.6192644754
## 24 1.438570 0.23 0.83240527 0.6061650846
## 25 1.439469 0.24 0.84442365 0.5950456894
## 26 1.434876 0.25 0.85487751 0.5799983308
## 27 1.435026 0.26 0.86634189 0.5686840954
## 28 1.429539 0.27 0.87555492 0.5539837309
## 29 1.420649 0.28 0.88490659 0.5357422293
## 30 1.408017 0.29 0.89140739 0.5166093947
## 31 1.395974 0.30 0.89802805 0.4979456847
## 32 1.384397 0.31 0.90488579 0.4795115263
## 33 1.374433 0.32 0.91049945 0.4639339906
## 34 1.363815 0.33 0.91650544 0.4473091662
## 35 1.353979 0.34 0.92150989 0.4324690076
## 36 1.343844 0.35 0.92609376 0.4177506848
## 37 1.333483 0.36 0.93054734 0.4029352606
## 38 1.324344 0.37 0.93479496 0.3895486641
## 39 1.315233 0.38 0.93853278 0.3767005617
## 40 1.307343 0.39 0.94248667 0.3648566854
## 41 1.297094 0.40 0.94494650 0.3521475901
## 42 1.290282 0.41 0.94892920 0.3413530833
## 43 1.281386 0.42 0.95165699 0.3297289435
## 44 1.273825 0.43 0.95500679 0.3188183219
## 45 1.261632 0.44 0.95815675 0.3034752161
## 46 1.258266 0.45 0.96109513 0.2971703685
## 47 1.252478 0.46 0.96314830 0.2893293768
## 48 1.246255 0.47 0.96532831 0.2809264056
## 49 1.237667 0.48 0.96683520 0.2708314010
## 50 1.227501 0.49 0.96937399 0.2581270566
## 51 1.217498 0.50 0.97128391 0.2462143416
## 52 1.209378 0.51 0.97408717 0.2352904954
## 53 1.201600 0.52 0.97586324 0.2257369595
## 54 1.194663 0.53 0.97803965 0.2166232054
## 55 1.188484 0.54 0.97995892 0.2085247657
## 56 1.181933 0.55 0.98181203 0.2001207422
## 57 1.172051 0.56 0.98290050 0.1891500474
## 58 1.166986 0.57 0.98447546 0.1825104543
## 59 1.161710 0.58 0.98584623 0.1758635743
## 60 1.151561 0.59 0.98754143 0.1640197833
## 61 1.144540 0.60 0.98897092 0.1555691450
## 62 1.136847 0.61 0.99033025 0.1465171714
## 63 1.129564 0.62 0.99163678 0.1379269467
## 64 1.123764 0.63 0.99266143 0.1311021410
## 65 1.118705 0.64 0.99361205 0.1250931499
## 66 1.116343 0.65 0.99442368 0.1219189654
## 67 1.111839 0.66 0.99510135 0.1167372546
## 68 1.104884 0.67 0.99557862 0.1093054758
## 69 1.101382 0.68 0.99632571 0.1050558275
## 70 1.097847 0.69 0.99693942 0.1009079029
## 71 1.092309 0.70 0.99720677 0.0951024170
## 72 1.087224 0.71 0.99755163 0.0896719782
## 73 1.081921 0.72 0.99782361 0.0840975133
## 74 1.076301 0.73 0.99809693 0.0782042477
## 75 1.068755 0.74 0.99843429 0.0703203494
## 76 1.060944 0.75 0.99856946 0.0623740672
## 77 1.054734 0.76 0.99877759 0.0559568445
## 78 1.048156 0.77 0.99891274 0.0492429634
## 79 1.043896 0.78 0.99911396 0.0447821331
## 80 1.037399 0.79 0.99958561 0.0378130625
## 81 1.030824 0.80 0.99965273 0.0311716465
## 82 1.023298 0.81 0.99979137 0.0235070196
## 83 1.019532 0.82 0.99986155 0.0196699574
## 84 1.013718 0.83 1.00000000 0.0137178941
## 85 1.010394 0.84 1.00000000 0.0103935772
## 86 1.007964 0.85 1.00000000 0.0079643730
## 87 1.005533 0.86 1.00000000 0.0055331786
## 88 1.004140 0.87 1.00000000 0.0041402898
## 89 1.002842 0.88 1.00000000 0.0028416366
## 90 1.002254 0.89 1.00000000 0.0022539061
## 91 1.001286 0.90 1.00000000 0.0012856521
## 92 1.000673 0.91 1.00000000 0.0006726964
## 93 1.000000 0.92 1.00000000 0.0000000000
## 94 1.000000 0.93 1.00000000 0.0000000000
## 95 1.000000 0.94 1.00000000 0.0000000000
## 96 1.000000 0.95 1.00000000 0.0000000000
## 97 1.000000 0.96 1.00000000 0.0000000000
## 98 1.000000 0.97 1.00000000 0.0000000000
## 99 1.000000 0.98 1.00000000 0.0000000000
## 100 1.000000 0.99 1.00000000 0.0000000000
## 101 1.000000 1.00 1.00000000 0.0000000000# this works but it does not show a group name
p = ggplot() +
geom_line(data = dfTestingResults, aes(x = Threshold, y = MeanSpec), color = "blue") +
geom_line(data = dfTestingResults, aes(x = Threshold, y = MeanSens), color = "red") +
geom_line(data = dfTestingResults, aes(x = Threshold, y = Sum), color = "green") +
xlab('Dates') +
ylab('percent.change') + theme_economist()
p
dfMeltThresholdData <- melt(dfTestingResults, id.vars="Threshold", value.name="value")
ggplot(dfMeltThresholdData, aes(x=Threshold, y=value, color=variable)) +
geom_point()